The trade-offs between the aerodynamic performance and aerodynamic noise signature of two-dimensional airfoil shapes in low-speed, high-lift flow are investigated. The figures of interest are calculated using Reynolds-Averaged Navier-Stokes-based computational fluid dynamics (CFD) simulations. A computationally efficient procedure for obtaining the Pareto front of the figures of interest is presented. The proposed approach utilizes a multi-objective evolutionary algorithm (MOEA) that works with a fast surrogate model of the aerodynamic surface under design, obtained with kriging interpolation of low-fidelity CFD simulations. The surrogate is enhanced by means of space mapping response correction based on a limited number of high-fidelity CFD simulation training points allocated in the design space. The Pareto set generated by the multi-objective optimization of the surrogate using MOEA is iteratively refined by local enhancements of the surrogate model. The proposed method allows us to obtain--at a low computational cost--a set of airfoil geometries representing the trade-offs between the figures of interest. We illustrate the approach using an example of an airfoil at a Mach number of 0.208, lift coefficient of 1.5, and a Reynolds number of 0.665 million.