Addressing the inefficient distribution of materials in workshops, a multi-objective optimization model with the shortest distribution path and the smallest time window penalty value was established. A hybrid optimization algorithm, INSGA-Ⅱ, based on a fast non-dominated sorting genetic algorithm (NSGA-Ⅱ) was proposed. Density peak clustering (DPC) was adopted to initialize the population and reduce the problem size. To avoid falling into local optimums, the differential evolution (DE) algorithm was used in the genetic operation stage of NSGA-Ⅱ. The differential operation of mutation vectors was used with partial mapped crossover to accelerate the iteration speed and improve the population diversity. Different benchmark functions were solved with different sizes of arithmetic cases, and the results showed that the improved algorithm had better Pareto front compared to the traditional NSGA-Ⅱ algorithm. Meanwhile, the results of the proposed algorithm had better uniformity and diversity, and the solution time was shorter. Experimental results showed that the proposed algorithm generated , compared with the NSGA-Ⅱ and the multi-objective particle swarm optimization (MOPSO), the total distribution distance could be reduced by up to 26.65% and the total time window penalty could be reduced by up to 32.5%. The new method can effectively improve the distribution efficiency of workshop material.