Aiming at shortcomings in global searching capacity and diversity of Pareto set existing in the traditional MOPSO and in order to enhance the diversity of pareto solutions, quasi-pareto solutions are constructed by sub-ant-colony algorithm which adopts its own and other sub-ant-colony heuristic information and quasi-pareto solutions obtained by every ant are used for control judegement. A crowding distance sorting based multi-objective particle swarm optimization algorithm is proposed. The constructed farther-group ants with the quasi-pareto solutions which act as space nodes constitute traveling salesman problem, hence, leading to the enhancement of the uniform distribution of pareto solutions. With the elitism strategy, the evolution of the external population is achieved based on individuals’ crowding distance sorting by descending order, to delete the redundant individuals in the crowding area. The update of the global optimum is performed by selecting an individual with a relatively bigger crowding distance, to lead the particles to evolve to the disperse region. Effectiveness of the algorithm with two and three objectives is proved by the optimization of three standard test problems. Comparison results illustrate that it outperformed NSGA-II and SPEA2 in the convergence and diversity characteristics of pareto optimal front.