Differences in working memory capacity (WM C) relate to performance on a variety of problem solving tasks. High WM C is beneficial for solving analytical problems, but can hinder performance on insight problems (DeCaro & Beilock, 2010). One suggested reason for WM C-related differences in problem solving performance is differences in strategy selection, in which high WM C individuals tend toward complex algorithmic strategies (Engle, 2002). High WM C might increase the likelihood of non- optimal performance on Luchins’ (1942) water jar task because high WM C 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 WM C model used a memory- intensive strategy, which led to long solutions when shorter ones were available. The low WM C model was unable to use that strategy, and switched to shorter solutions.