A suitable green supply chain network can significantly affect both the supply chain and the environment. Such network should guide the supply chain towards an efficient and effective management to increase the profit, desirable impacts on the environments and responsiveness to the customers' requests. In this research, a green supply chain with limited greenhouse emission was designed which could reduce the chain costs by simultaneous selection of the supplier and carriers with various capacities. Thus, in this research a bi-objective nonlinear programming model was proposed which is aimed to select the carriers between the chain levels and select the supplier based on the quality of the consumed material. Moreover, the delivery time to customers and emission from transportation and production were the other constraints of the problem. First goal was to minimize the total chain costs while reduction in the rejection of the consumed material was in the second rank. In order to validate the proposed model, several numerical problems were randomly generated and solved using GAMS optimization software. Since the problem is NP-hard and its solution time increased exponentially by increase of the problem dimension, a multi-objective meta-heuristic imperialist competitive algorithm was proposed to solve the problem in large scales. Crowding distance was also used to rank the solutions of one front. Computational results and comparisons by indices like mean distance from the ideal were employed to describe the algorithm efficiency. The results showed that features such as carrier selection and environmental factors can enable the decision process of supplier selection to be well approximated with the real-world situation, showing the potential usefulness of these concepts
