Abstract: The NSGA-Ⅱ algorithm has poor discrimination on the solution set during dealing with high-dimension multi-objective evolutionary problems. Aiming at the above shortcoming, a generalized Pareto domination optimization algorithm based on the expansion angle (GPO-NSGA-Ⅱ) was proposed, whose feature is to change the expansion angle so as to adjust the dominance area of solutions and raise the degree of discriminability. In the evolutionary process of the GPO-NSGA-Ⅱ algorithm, the algorithm's expansion angle will remain constant. In this paper, we propose a dynamic generalized Pareto domination optimization algorithm, DGPO-NSGA-Ⅱ. By dynamically adjusting the expansion angle in the population evolution process, the selection pressure of the population evolution may be affected. The dynamic adjustment of the expansion angle is linearly reduced, that is, the expansion angle is decreased linearly from the initial expansion angle to 0 as the population evolves. In order to ensure a better initial expansion angle interval, a large number of comparative experiments are carried out on the different expansion angles of population evolution. Finally, by comparing with GPO-NSGA-Ⅱ and NSGA-Ⅱ in the test function, the proposed algorithm can converge to the theoretical front with the higher precision, and the distribution of the individual is more uniform.