This paper integrates production, distribution and logistics activities at the strategic decision making level, where the objective is to design a multiechelon supply chain network considering agility as a key design criterion. A network with five echelons of supply chains including suppliers, plants, distribution centers, cross-docks, and customer zones is addressed in this paper. The problem has been mathematically formulated as a biobjective optimization model that aims to minimize the cost (fixed and variable) and maximize the plant flexibility and volume flexibility. A novel multiobjective parallel simulating annealing algorithm (MOPSA) is proposed to obtain the Pareto-optimal solutions of the problem. The performance of the proposed solution algorithm is compared with two well-known metaheuristics, namely, nondominated sorting genetic algorithm (NSGA-II) and Pareto archive evolution strategy (PAES). Computational results show that MOPSA outperforms the other metaheuristics.