The tension between habits and plans is reflected in everyday decision-making. Habits are computationally cheap, but fail to flexibly adapt to changes in the environment. Planning is a flexible decision-making strategy, but requires greater resources. Arbitration between habits and plans has been formalized using reinforcement learning algorithms that distinguish between model-free control (habits) and model-based control (plans). Evidence about these two decision-making approaches suggests model-based control follows a developmental trajectory, emerging during adolescence, strengthening during young adulthood, and declining in older adulthood. The normative decline in planning (model-based control) presents the opportunity to develop interventions to increase flexible decision-making. Therefore, we asked if incentives could be used to increase model-based control in older adults. We expected older adults would fail to increase model-based control in response to incentives. This prediction was based upon prior research suggesting older adulthood is associated with deficits in representing and updating the expected value of rewards. Contrary to our expectations, in Experiment 1 we found that incentives could be used to boost model-based control in older adults sampled from an online population. We hypothesized this may be due to previous experience with the task (or with similar tasks). In Experiment 2, a naïve sample of older adults did not boost model-based control in response to incentives. These results suggest that incentives may be a useful intervention to increase model-based planning in older adulthood, but this may require extensive experience with the incentive structure.