Complex systems are challenging for engineers to understand and design. This work demonstrates a synergistic cognitive and agentbased methodology for developing and implementing rule-based strategies that improve human search performance in optimization design tasks. The domain of our study is the design of synthetic myosin-based systems, the biologically-based building block of muscle. We began with an initial cognitive study of users solving design tasks with three varied difficulties using a graphical user interface, and tracked how they manipulated design variables in their search process. User search behaviors resulting in the best and worst designs were then examined. Trends were identified that were used to formulate three strategies automated by computational agents solving the same tasks as the users. The most successful identified strategy implemented by the agents was a combination of univariate searches to learn parameter relationships and then applying that knowledge in greedy local searches. On one of the three tasks, an initial random search improved results. A subsequent cognitive study was conducted with users implementing the best agent-tested strategies. Users implementing the strategy performed significantly better than users performed in the first study with no provided strategy. These results show the power of synergistic human and agent-based approaches, in that cognitive-based findings can provide a starting place for computational search algorithms to begin testing strategies. Experimentation through agent-based methods via fast and extensive automated searches can then produce effective strategies that are given back to users. Our primary findings demonstrate that these agenttested strategies significantly improve human search performance in designing these complex systems.