Like most real-life processes, the operation of liquid-solid circulating fluidized bed (LSCFB) system for continuous protein recovery is associated with several objectives such as maximization of production rate and recovery of protein, and minimization of amount solid ion-exchange resin requirement, all of which need to be optimized simultaneously. In this article, multiobjective optimization of a LSCFB system for continuous protein recovery was carried out using an experimentally validated mathematical model to find the scope for further improvements in its operation. Elitist non-dominated sorting genetic algorithm with its jumping gene adaptation was used to solve a number of bi- and tri-objective function optimization problems. The optimization resulted in Pareto-optimal solution, which provides a broad range of non-dominated solutions due to conflicting behavior of the operating parameters on the system performance indicators. Significant improvements were achieved, for example, the production rate at optimal operation increased by 33%, using 11% less solid compared to reported experimental results for the same recovery level. The effects of operating variables on the optimal solutions are discussed in detail. The multiobjective optimization study reported here can be easily extended for the improvement of LSCFB system for other applications.