This article presents a new methodology for designing industrial networks and analyzing them dynamically from the standpoint of sustainable development. The approach uses a combination of optimization and simulation tools. Assuming “top-down” overarching control of the network, we use global dynamic optimization to determine which evolutionary pathways are preferred in terms of economic, social, and environmental performance. Considering the autonomy of network entities and their actions, we apply agent-based simulation to analyze how the network actually evolves. These two perspectives are integrated into a powerful multiscale modeling framework for evaluating the consequences of new policy instruments or different business strategies aimed at stimulating sustainable development as well as identifying optimal leverage points for improved performance of the network in question. The approach is demonstrated for a regional network of interdependent organizations deploying a set of bioenergy technologies within a developing-economy context.