Considering the uncertainty of transportation time and cost due to seasonality and human factors, A multi-objective chance constrained programming model with minimum transportation time and cost was established. According to the characteristics of the problem, the beetle search algorithm with one beetle is changed into multiple beetles and Dijkstra algorithm is embedded, a hybrid beetle swarm optimization algorithm (HBSO) is designed to solve the problem, and the case analysis and algorithm analysis are made for three different examples. By adjusting the model parameters, the minimum objective function value under the combination of parameters is obtained. Under the three examples, the influence of the customer on the time and cost is different, and the impact on the whole logistics and transportation scheme is obtained. The Taguchi test was used to determine the optimal parameter combination of the HBSO under different node scales. The GA and PSO are embedded with Dijkstra algorithm, and are compared with HBSO to verify the optimization ability of the HBSO.