Multi-objective optimization of a solid oxide fuel cell (SOFC) reactor for oxidative coupling of methane has been studied with the elitist non-dominated sorting genetic algorithm with jumping genes (NSGA-II-aJG). A parametric sensitivity analysis was carried out on the experimentally verified model to systematically investigate the effects of the process parameters on the performance of the SOFC reactor. Several two and three objective optimization problems were performed using NSGA-II-aJG. Significant performance improvement in terms of C-2 yield and electrical power could be achieved when rigorous optimization was performed. These sets of solution narrow down the choices available to a decision maker, who can choose the 'preferred' solution among the points in the set. (C) 2010 Published by Elsevier B.V.