Differences in working memory capacity (WMC) relate to performance on a variety of problem solving tasks. High WMC is beneficial for solving analytical problems, but can hinder performance on insight problems (DeCaro & Beilock, 2010). One suggested reason for WMC-related differences in problem solving performance is differences in strategy selection, in which high WMC individuals tend toward complex algorithmic strategies (Engle, 2002). High WMC might increase the likelihood of non-optimal performance on Luchins’ (1942) water jar task because high WMC solvers tend toward longer solutions, not noticing when shorter solutions become available. We present empirical data showing this effect, and a computational model that replicates the findings by choosing among problem solving strategies with different WM demands. The high WMC model used a memory-intensive strategy, which led to long solutions when shorter ones were available. The low WMC model was unable to use that strategy, and switched to shorter solutions.