Computational capability has been developed to automatically determine a good estimate of the core loading pattern, which minimizes fuel cycle costs for a pressurized water reactor (PWR). Equating fuel cycle cost minimization with core reactivity maximization, the objective is to determine the loading pattern that maximizes core reactivity while satisfying power peaking, discharge burnup, and other constraints. The method utilizes a two-dimensional, coarsemesh, finite difference scheme to evaluate core reactivity and fluxes for an initial reference loading pattern. First-order perturbation theory is applied to determine the effects of assembly shuffling on reactivity, power distribution, and end-of-cycle burnup. Monte Carlo integer programming is then used to determine a near-optimal loading pattern within a range of loading patterns near the reference pattern. The process then repeats with the new loading pattern as the reference loading pattern and terminates when no better loading pattern can be determined. The process was applied with both reactivity maximization and radial power-peaking minimization as objectives. Results on a typical large PWR indicate that the cost of obtaining an 8% improvement in radial power-peaking margin is ∼2% in fuel cycle costs, for the reload core loaded without burnable poisons that was studied.