Related research shows that general many-objective optimization evolutionary algorithms cannot effectively process different types of Pareto fronts (PFs). To address this problem, we propose a many-objective evolutionary algorithm based on population association strategy and enhanced solution set criterion，named MaOEA/PAS-ESC. In the environment selection, the population association strategy (PAS) and enhanced solution set criterion (ESC) are used to cooperatively guide population evolution. For the purpose of trade-off between diversity and convergence, PAS uses the angles, Euclidean distances between solutions and reference vectors and the distances between solutions in current population to maintain diversity. And ESC uses the association between reference points and current population to select solutions with good convergence. The experimental results prove that MaOEA/PAS-ESC not only has more competitive performance in dealing with many-objective optimization problems, but also improves the ability to handle different types of PFs.