The performance of reactive simulated moving bed (SMBR) process was optimized for an experimentally verified mathematical model for the synthesis of methyl acetate ester. Multiobjective optimization was performed for an existing SMBR experimental setup, and optimum results obtained were subsequently verified experimentally. Thereafter, few other multiobjective optimization studies were performed for both existing setup and at the design stage. The effect of variable (distributed) feed flow rate on the optimum performance of SMBR was also investigated. The optimization was performed using AI-based nondominated sorting genetic algorithm (NSGA), which resulted in Pareto optimal solutions. The paper demonstrates usefulness of multiobjective optimization in the design of reactive SMB processes.