Abstract: The simulated moving bed (SMB) chromatography has been used extensively as a main modern separation technology for the complex separation tasks in the areas of petroleum chemicals, fine chemicals, pharmaceuticals, and food processing in recent years. Compared with the conventional batch chromatographic separation process, the simulated moving bed chromatography is preferred for its continuous operations. The SMB technology has the advantage of significantly lower solvent and solid phase consumption while delivering higher purity and productivity. It also achieves a faster scale-up production especially for the separation of homologues and chiral enantimers which are difficult to separate. The simulated moving bed process is a highly sophisticated hybrid process that includes both continuous and discrete variables. The separation effects are subject to the influence of operation parameters such as the absorption isotherm, column characteristics, the feed concentration, feed ingredient, temperature, and switch time, between which complex dynamics as well as strong couples exist. These influence factors inevitably increases the experiment cycle, and makes the process modeling, operation optimization, and industry enlargement extremely difficult. Moreover, the simulated moving bed process is highly sensitive and uncertain to external disturbances and the establishment and implementation of robust control methods for the SMB process is also a potential research area. The difficulty of applying conventional feedback control to the SMB process lies not only in its non-linearity, the cyclical stable state, the presence of both continuous and discrete variables, but also in the large time delays to disturbances. Therefore, the modeling and optimization research of the simulated moving bed separation process not only has important theoretic values and enormous economic benefits. Due to the complexity of simulated moving bed model and performance index of diversity, intelligent algorithm is used to optimize performance of the simulated moving bed production. In this paper, GA algorithm and PSO algorithm are used to optimize single objective of simulated moving bed under linear adsorption isotherm. Then, by comparing the particle swarm optimization with the genetic algorithm, the superiority of quantum theory in solving the simulated moving bed optimization problem is proved.NSGAⅡ algorithm,MOPSO algorithm are used to optimize multiple objectives of simulated moving bed under linear adsorption isotherm. Simulation results show that these methods are valid.