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How to Model Credit Card Rewards: A Systematic Strategy

A systematic approach to comparing credit card rewards under stated spending and redemption assumptions.

Most People Leave $500-$2,000/Year on the Table

The average American household spends about $6,000/month across all categories. With the right card setup, you could earn 2-4% effective cash back on that spend — $1,440 to $2,880/year. But most people earn under 1.5% because they use one or two cards without thinking about category optimization.

The Multi-Card Strategy

The key insight is that no single card leads every category. The Amex Gold earns 4x on dining and groceries but only 1x on gas. The Chase Freedom Unlimited earns 1.5x everywhere but nothing bonus. A modeled strategy can use 3-5 cards, each covering categories where it earns a high rate after the stated assumptions.

Step 1: Know Your Spending

Track your monthly spending by category. The big ones for most people:

  • Dining/restaurants — including takeout and delivery
  • Groceries — supermarkets (not Walmart/Target, which code as general merchandise)
  • Gas — filling stations
  • Travel — flights, hotels, car rentals
  • Online shopping — Amazon, general e-commerce
  • Streaming — Netflix, Spotify, etc.
  • Everything else — the base rate category

Step 2: Match Cards to Categories

For each high-spend category, find the card with the best earn rate after accounting for annual fees and spending caps.

Common optimal assignments:

  • Dining: Amex Gold (4x), Capital One Savor (3%), or Citi Strata Premier (3x)
  • Groceries: Amex Gold (4x up to $25K), Blue Cash Preferred (6% up to $6K), or Capital One Savor (3%)
  • Gas: Costco Anywhere Visa (4%), BofA Customized Cash (pick gas for 2-5.25%)
  • Travel: Chase Sapphire Reserve (4x flights & hotels + portal), Venture X (2x + credits), or Amex Platinum (5x flights)
  • Everything else: Chase Freedom Unlimited (1.5x UR), Citi Double Cash (2%), or Wells Fargo Active Cash (2%)

Step 3: Account for Ecosystems

Cards within the same ecosystem (Chase Ultimate Rewards, Amex Membership Rewards, Capital One Miles) can pool points. This matters because having a premium card like the CSR or Venture X makes the points from lower-tier cards more valuable through transfer partners.

This is where manual optimization gets hard — should you pay $95 for the CSP to unlock transfers for CFU points? The answer depends on how many CFU points you earn, your transfer confidence, and the marginal cost.

Step 4: Net Out Fees

A card that earns $500/year but costs $550/year is worse than a free card earning $400. Always compare net value (earnings minus effective annual fee). Account for credits you’ll actually use — a $300 travel credit is worth $300 only if you’d spend that on travel anyway.

Why You Should Use an Optimizer

Steps 1-4 are manageable for comparing 2-3 cards. But when you have dozens of cards with different earn structures, caps, tiered rates, and fee credits, the interactions become exponentially complex.

OptimalCardSetup handles these interactions with a mathematical constraint model. Enter your spending and the solver searches eligible setups, then reports whether the result was proven optimal or returns the strongest valid setup found within its time limit while stating that optimality was not proven. It replaces hours of spreadsheet work while preserving that important distinction.

Try the optimizer now — it’s free for up to 2 cards.

Not financial advice. OptimalCardSetup provides mathematical optimization tools for educational and informational purposes only. This does not constitute financial, investment, or credit advice. Card rates, fees, and benefits shown are last checked as of Aug 2, 2026. Terms may change — always verify current details with the card issuer before applying.

Solver examples on this site are bounded by the stated catalog, objective, spending assumptions, redemption values, constraints, and data snapshot. If a run timed out, optimality was not proven; issuer eligibility, approval, terms, and application-channel offers remain controlling.

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