Many real-world problems are defined by complex systems of interlocking constraints. How people are able to solve these problems with such limited working memory capacity remains poorly understood. We propose a formal model of human problem-solving that uses metacognitive knowledge of its own memory limits and imperfect reasoning to guide subproblem choice. We compare our model to human gameplay in two experiments using a variant of the classic game Minesweeper. In Experiment 1, we find that participants’ accuracy was influenced both by the order of subproblems and their ability to externalize intermediate results, indicative of a memory bottleneck in reasoning. In Experiment 2, we used a mouse-tracking paradigm to assess participants’ subproblem choice and time allocation. The model captures key patterns of subproblem ordering, error, and time allocation. Our results point toward memory limits and strategies for navigating those limits— including the careful choice of subproblems and memory-offloading — as central elements of human problem-solving.