In practice, when faced with a complex optimization problem, human decision-makers often separate it into subproblems and then solve each subproblem instead of tackling the complete problem. This paper describes a study that simulated small teams of bounded rational decision-makers (“agents”) who try different approaches to solve optimization problems. In the “all-at-once” approaches, the agents collaborate to search the entire set of solutions in a sequential manner: each agent begins where the previous agent stopped. In other approaches, the agents separate the problem into subproblems, and each agent solves a different subproblem. Finally, in the hybrid approaches, the agents separate the problem but two agents will collaborate to solve one subproblem while another agent solves a different subproblem. In some cases, the subproblems are solved in parallel; in others, the subproblems are solved sequentially. The results show that the teams generally found better solutions when they separated the problem.