Multi-objective optimization for the operation and design of an industrial-scale Parex process for the recovery of p-xylene from a mixture of C-8 aromatics has been studied using a state-of-the-art optimization technique: the Elitist Nondominated Sorting Genetic Algorithm with Jumping Genes (NSGA-II-JG, binary coded). An industrial-scale Parex process, which is based on simulated moving bed (SMB) technology, was simulated using an available mathematical model. The simulation results were verified with the reported industrial data. Subsequently, few two-objective optimization problems were solved to determine the optimum number of columns, the length of each column, and the column distribution in different zones. Significant improvement in the recovery of p-xylene using less solvent was obtained when a suitable column length and appropriate distribution of columns in various zones was chosen. Optimization study was also extended for the Varicol operation mode, which is based on a nonsynchronous switching, contrary to the synchronous switching used in traditional SMB systems. It was observed that the performance of the Varicol process is much superior to that of a SMB process, in terms of recovering more p-xylene while consuming less eluent. Results are presented and discussed in detail. The optimum results are also explained using equilibrium theory by locating them in the pure separation region.