Multi-objective optimization is an important topic of process systems engineering.Through simply converting to a single goal，it often fails to reflect the more complex relationship between goals，and it is not conductive to effective decision-making at any time on demand.Swarm intelligence based particle swarm optimization（PSO）algorithm has good global optimization performance and can be easily implemented.To make it suitable for multi-objective optimization，PSO should be further rebuilt.Firstly，the concept of Pareto dominance was used to evaluate the fitness of particles，and two kinds of operation for determining the local and global optimal point respectively were designed.Secondly，velocity update strategy，utilizing all particles’ local best information，was used to enhance the ability of global convergence.Thirdly，an external archive technique was set up，and through calculating the degree of dispersion，an appropriate update strategy was adopted to uniformly approximate the Pareto optimal solution set step-by-step.Finally，multi-objective particle swarm optimization（MOPSO）was proposed，and it was applied to dynamic multi-objective optimization of fed-batch bioreactor，the satisfactory solution was obtained.According to the obtained pareto optimal solution set，the relationship between goals could be analyzed further，which could contribute to rational and effective decision-making.Compared with NSGA-Ⅱ，MOPSO showed better performance.