Research shows that the general many-objective evolutionary algorithm can not effectively deal with different types of Pareto fronts. In view of the above situation, this paper proposes a many-objective evolutionary algorithm based on the population association strategy and enhanced solution set criterion(MaOEA/PAS-ESC). In this algorithm, the population association strategy(PAS) and enhanced solution set criterion(ESC) are used to guide the population evolution. The PAS uses the angle and Euclidean distance between the solution and the reference vector as well as the distance between the solutions in the population to construct the joint function of angle and distance (JFAD) and select the solution with good diversity. Then, the ESC uses the connection between the reference point and the population to form the fitness function and select the solution with good convergence, in order to balance diversity and convergence effectively. The experimental results show that the MaOEA/PAS-ESC not only has stronger competitive performance in dealing with many-objective optimization problems, but also improves the ability to deal with different types of Pareto fronts.