An efficient methodology is presented to generate the Pareto-optimal hypersurface of the performance space of a complete mixed-signal electronic system. This Pareto-optimal front offers the designer access to all optimal design solutions: starting from the performance specifications, a satisfactory point can a posteriori be selected on the hypersurface which immediately determines the final design parameters. Fast execution is guaranteed by using multi-objective evolutionary optimization techniques and hierachical decomposition. The presented method takes advantage of the Pareto hypersurfaces of the subblocks to generate the overall Pareto front. The hierarchical approach combines behavioral simulation with behavioral models at the higher levels, with SPICE simulations with transistor-level accuracy at the lowest level. Storing the performance data of all subblocks enables reuse for other systems later on.