Human and animal behaviors are influenced by goal-directed planning or automatic habitual choices. Reinforcement learning (RL) models propose two distinct learning strategies: a model-based strategy, which is more flexible but computationally demanding, and a model-free strategy is less flexible yet computationally efficient. In the current RL tasks, we investigated how individuals adjusted these strategies under varying working memory (WM) loads and further explored how learning strategies and mental abilities (WM capacity and intelligence) affected learning performance. The results indicated that participants were more inclined to employ the model-based strategy under low WM load, while shifting towards the model-free strategy under high WM load. Linear regression models suggested that the utilization of model-based strategy and intelligence positively predicted learning performance. Furthermore, the model-based learning strategy could mediate the influence of WM load on learning performance. These findings underscore the critical role of WM capacity in strategic selection during RL process.