The performance of the evolutionary technique, multiobjective simulated annealing (MOSA), is improved by the use of adaptations inspired by the concept of jumping genes in biology, because of the added diversity. A computationally intensive, real-life, two-objective problem in chemical engineering, namely, the optimization of an industrial steam reformer operating under unsteady-state conditions, is studied. Sets of nondominated solutions obtained using MOSA and its jumping gene (JG) adaptations are compared with those obtained earlier using a nondominated sorting genetic algorithm (NSGA-II). Good agreement between the results obtained by several algorithms is observed. The aJG adaptation of MOSA leads to nondominated optimal solutions having a good maximum spread and reasonable spacing of the points.