A hybrid multiobjective evolutionary approach to the design optimization of a seven-pin wire-wrapped fuel assembly is applied to achieve an acceptable compromise between two conflicting objectives: enhancement of heat transfer and reduction of pressure drop. Two nondimensional variables, the ratio of wire-spacer diameter to fuel rod diameter and the ratio of wire-wrap pitch to fuel rod diameter, are chosen as design variables. The Latin hypercube sampling method is used to determine the training points. The response surface method is used to approximate the Pareto-optimal front with Reynolds-averaged Navier-Stokes analysis of the flow and heat transfer. The shear stress transport turbulence model is used as turbulence closure. The optimization results are processed by the Pareto-optimal method. The Pareto-optimal solutions are obtained using a combination of the evolutionary algorithm NSGA-II and a local search method. The Pareto-optimal front for the wire-wrapped fuel assembly has been obtained. Six optimal values of the design variables have been obtained using clustering. With the increase in the wire-spacer diameter, both heat transfer and pressure drop in the assembly increase. Increasing the wire-wrap pitch reduces the pressure drop in the assembly at the cost of heat transfer.