In the last few years, multiobjective evolutionary algorithms (MOEAs) have gained significant interest as a reliable option to optimize problems with conflicting objectives in science and engineering. These algorithms generate an optimal set of trade-off solutions referred to as the Pareto domain. In this investigation, a MOEA was used to optimize simultaneously conflicting design variables of an industrial styrene reactor. The dual population evolutionary algorithm (DPEA) was implemented to optimize the productivity, yield, and selectivity of styrene. To evaluate the robustness and versatility of the algorithm, two and three objective optimization case studies were conducted for three different configurations of the reactor: adiabatic, steam-injected, and isothermal.Results indicated that DPEA is a robust optimization strategy to generate a well-defined Pareto domain with a wide range of solutions. In addition, the Pareto-optimal solutions of the steam-injected configuration were superior to the adiabatic reactor and to a portion of the isothermal configuration. The optimal operating conditions corresponding to the Pareto domains were also slightly better in terms of profit when compared with previously published studies. The Pareto domains were then ranked using the Net Flow Method (NFM), a ranking algorithm that incorporates the knowledge and preferences of an expert into the optimization routine.