Abstract: In order to achieve multi-contradiction-objective optimization scheduling of logistics resources under the constraints of production processes, a density adaptive MOEA/D algorithm scheduling method was proposed. An analysis was conducted on the logistics scheduling problem under production process constraints in an intelligent workshop, and an optimization scheduling model was established with multiple conflicting objectives such as minimizing completion time, the number of logistics vehicles, and penalty costs. Based on the MOEA/D algorithm, a penalty factor that adapts to changes in chromosome density in the neighborhood was designed to regulate chromosome diversity and algorithm convergence, effectively improving the quality of the algorithm’s solution set. The density adaptive MOEA/D algorithm was applied to logistics resource scheduling and experimentally validated. The results showed that compared with the MOEA/D algorithm and the improved NSGA-II algorithm, the density adaptive MOEA/D algorithm has higher quality of solution set and better distribution diversity. Taking 3 logistics vehicles as an example, the density adaptive MOEA/D scheduling scheme has the shortest completion time of 749 min. The experimental results demonstrate the superiority of the method proposed in this paper in optimizing and scheduling logistics resources with multi-contradiction-objective.