Differential evolution is a simple and powerful globally optimization new algorithm. It is a population-based, direct search algorithm, and has been successfully applied in various fields. A multi-objective differential evolution algorithm based on max-min distance density is proposed. The algorithm proposed defines max-min distance density and gives a Pareto candidate solution set maintenance method, ensuring the diversity of the Pareto solution set. Using Pareto dominance relationship among individuals and max-min distance density, the algorithm improves the selection operation of differential evolution, ensures the convergence of the algorithm, and realizes the solution of multi-objective optimization problems by differential evolution. The proposed algorithm is applied to five ZDT test functions and two high dimension test functions, and it is also compared with other multi-objective evolutionary algorithms. Experimental result and analysis show that the algorithm is feasible and efficient.