Considering today's globalized and highly uncertain business environment, the supply chain network is prone to the risk of disruption. At the same time, in view of the issues, such as demand fluctuation, high return rate and environmental pollution caused by improper treatment, the strategies to mitigate the interruption risk were proposed and a multi-objective closed-loop supply chain network model of perishable products under uncertain conditions concerning minimum economic cost, the minimum environmental impact and the maximum social benefit was constracted. In order to reduce the influence of uncertain parameters and realize the decision-making arrangement of the optimal facility location and the optimal distribution path, the demand and return quantity of perishable products were set as triangular fuzzy values, and the fuzzy constraints in the model were equivalently transformed into clear correspondences by using fuzzy chance constraint method. Taking a perishable product enterprise in Shanghai as an example, genetic algorithm (GA) and particle swarm optimization (PSO) algorithm were used to solve the model. The calculating results show that the performance of supply chain network can be significantly improved by using the strategy of mitigating disruption risk, and the overall performance of multi-objective optimization of closed-loop supply chain network is better than that of single objective optimization.