The NSGA-II-DE algorithm improves the convergence speed and reduces the dependency on the parameters of NSGA-II by taking advantage of the fast convergence and high robustness of the DE algorithm. However, the solutions distribution of the original algorithm is not improved. In this study, an advanced NSGA-II-DE algorithm based on the population expansion and sparsity strategy is proposed. This algorithm increases the number of candidate solutions by using population expansion operation, and the sparsity strategy is used to get optimum solutions from candidates that make the global distribution as uniform as possible. Population expansion is achieved by preserving individuals on the first non-dominated surface of each generation in the last few generations of evolution. At the end of the iteration, the population is non-dominated sorted, and the individuals outside the first non-dominating surface are removed to improve the quality of the solutions. Then, the sparseness is processed, that is, all the individuals after expansion are sorted according to a certain dimension of the target vector, and the individuals whose adjacent distance closest to the expected distance are selected, so as to improve the distribution of the solutions. The simulation results show that the proposed algorithm is superior to the original algorithm in terms of the distribution of solutions, but inferior to the original algorithm in terms of the time and space complexity.