In this paper, we present a study on the aerodynamic shape optimization of a three-dimensional subsonic engine nacelle using computational fluid dynamics simulations. Gaussian process-based surrogate modeling (kriging) and parameter screening techniques are combined to tackle the high cost associated with both computational fluid dynamics simulations and the large number of design variables involved, with a multi-objective genetic algorithm being used to obtain the Pareto fronts. The primary goal of the study was to identify the tradeoff between aerodynamic performance and noise effects associated with various geometric features within practical computational costs. The fan face total pressure recovery is used to measure the aerodynamic performance, and the scarf angle is used as an indicator of the noise impact on the ground. The geometry is modeled using a feature-based parametric computer-aided design package. An unstructured tetrahedral mesh is generated for the subsequent solution using the Reynolds averaged Navier–Stokes flow equations. Analyses of variance techniques are used to identify the dominant geometry parameters, thereby reducing the number of design variables and computational cost in the trade study. Multiple Pareto fronts are constructed using progressively built kriging models based on simulation data with the reduced parameter set. A full-scale search was also carried out for comparison with the results produced using the reduced parameter set. The procedures outlined can be further applied to other optimization problems with significant numbers of parameters and high-fidelity analysis codes.