The feasibility of system-level optimization in complex engineering design rests on the capability to iteratively converge a system of coupled subprocesses in a reasonable amount of time and at a reasonable cost. Each of these subprocesses, or modules, has an individual time and cost to execute. Convergence strategies are systems of rules for executing through the set of modules. Strategies can be developed which draw information from system characteristics to reduce the overall time and cost of converging the system. A simulator has been created which combines automatic model building and sequencing capability with an engine capable of running either sequential or parallel execution strategies. Several first generation strategies, both sequential and parallel, are explored using this technique.