The simulation-based design of chemical processes with an overlaid multi-objective optimization is of growing importance. The quality of the determined optimization results, referred to as the Pareto front, influences directly the variety of solutions given to the decision maker. In case of the frequently used genetic algorithms, a lot of studies have been done for a precise approximation of the Pareto front. However, most of the developed methods are inconvenient and/or time-consuming and, thus, inappropriate for industrial applications. A novel approach, the two-step optimization, was successfully tested in order to overcome these challenges.