The ability to predict, reason about, and act in the physical world is crucial for human survival, but the cognitive systems that underlie these capabilities have been the subject of intense debate. Some theories posit that reasoning about physical events is based on dynamic mental models that approximately simulate underlying physical mechanisms (e.g., the forces incident on objects that cause them to move); others argue for simpler heuristics that predict key physical outcomes (e.g.,“objects fall straight down when dropped”). We argue that general physical reasoning requires both simulation and rules, and propose a modeling framework for understanding the interactions and trade-offs between these cognitive systems as resource-rational computations to efficiently solve problems. We study these trade-offs using predictions about stability: judging whether a balance beam will fall, and if so how. While prior research suggests that people often use rules when solving balance beam tasks, these tasks are similar to others that have been found to rely on mental simulation. Across five experiments, participants’ predictions cannot be explained with simulation or rules alone, but we find evidence that individuals rely on both capacities. The mixture of strategies that people use to solve these stability problems is consistent with a resource-rational trade-off that accounts for the costs and benefits of using those strategies. Finally, we find that participants can rationally adapt this mixture of strategies to perform more efficiently given the distribution of task instances they encounter, demonstrating the flexible and online nature of the computational trade-offs in intuitive physics.