<p>Supply Chain Network Design (SCND) has been a popular research subject. The inclusion of various features has added to its attractiveness to make it closer to the real world. In this study, the shortage was considered lost sales for customer demand due to reduced quality of perishable products based on the exponential function of their life. The considered problem was formulated in a bi-objective Mixed-Integer Nonlinear Programming (MINLP) model that simultaneously minimized supply chain costs and the amount of Green House Gas (GHG) emissions. To solve the problem, a heuristic-based meta-heuristic algorithm, namely Multi-Objective Chaos Game Optimization (MOCGO), was developed and implemented to improve the solving time and the quality of the obtained solutions. This proposed algorithm was based on the Simple Additive Weighting (SAW) and Shannon Entropy approaches to select an optimal solution from the Pareto front and replace this approach with the crowding distance and non-dominated sorting in the main iterations of the algorithm to reduce the complexity function of the algorithm. Finally, eight criteria were introduced to compare the performance of the proposed algorithm with that of other meta-heuristics, and also the results of solving different examples were examined. The results indicated that a combination of heuristic and meta-heuristic algorithms improved the efficiency of the proposed meta-heuristic algorithm. Moreover, the results showed that the MOCGO algorithm outperformed most problems compared to other algorithms in terms of solving time, solution diversity, solution quality and solution convergence.</p>