Todays, industries are seeking the ways to improve their competitiveness and responsiveness in order to achieve the most share of markets and customer satisfaction. Optimization of strategic and tactical decisions in a logistics network would improve total performance of the supply chain in a long term planning horizon. This paper presents a Mixed-integer linear programming (MILP) model to optimize logistics networks under real limitations such as demand, capacity, and budget constraints. Due to NP-hard nature of the proposed model a Differential Evolutionary (DE) algorithm is proposed to solve the large sizes of the presented model in reasonable time. Finally, the computational results obtained through the DE algorithm are compared with the solutions obtained by GAMS optimization software. The results reveal that the proposed methodology is an efficient tool to optimize large scale logistics networks.