In view of the shortcomings of existing algorithms for high-dimensional multi-objective problems such as poor diversity and complex computation, an intelligent optimization algorithm based on conflict information partition and high-dimensional multi-objective parallel evolution is proposed. The objective space is divided into several subintervals by using the conflict information among the objectives, and the independent evolution is used to reduce the difficulty of solving the problem; the aggregation information of other subintervals is added to each subinterval, and the global information is considered to avoid local convergence; then according to the different number of objectives in the subinterval, the parallel independent optimization algorithm is used to reduce the search space, so as to avoid weakening the role of the evolution operator. The optimization performance of the algorithm is improved. Three classical algorithms are compared with this algorithm to verify its advantages and disadvantages. The comparison results show that the algorithm can reduce the pollution in the vehicle routing, and can realize the enterprise cost minimization while meeting the customer requirements.