Many-objective optimization problems generally refer to problems with four or more objectives. Because the number of nondominated solutions increases quickly with the increase of the number of objectives, the evolutionary pressure and efficiency of multi-objective evolutionary algorithms decrease heavily. To solve this problem, this paper proposes a reference-point-based particle swarm optimization algorithm for many-objective optimization. A structured set of reference points is generated in the objective space, and then the solutions which have both good convergence and diversity are selected as the global best to increase the selection pressure. Meanwhile, a truncation method for the external archive based on the reference points is proposed to maintain the diversity of the solution set. The results of simulations on the DTLZ2 test problem show that the proposed algorithm can obtain a solution set which has both good convergence and diversity for many-objective optimization problems.