Recently much research has focused on both the supply chain and reverse logistics network design problem. The rapid progress in computer and network technology and the increasingly fierce competition in recent times have compelled global company to consider these two networks in integrated view for efficient decision-making throughout the supply chain. The integrated problem, however, resembles a combinatorial problem, whose computation time to obtain an optimal solution increases exponentially in proportion to the size of the problem. Therefore, an algorithm able to generate a relatively good solution within a reasonable time is needed. In this study, we propose an LPbased genetic algorithm. The experimental results show that the proposed algorithm is superior to MIP solver in time and to traditional genetic algorithm in quality.