Individual patients, care providers, and other stakeholders can benefit from the development and implementation of data-driven optimal treatment strategies. Optimal treatment regimes can improve outcomes and lower healthcare costs through optimal treatment decision rules, which maximize a population-level distributional summary such as the expected value of a clinical outcome. Guidance for estimating optimal decision rules in the presence of missing data is fairly limited, as the majority of existing methods rely on having a complete set of data that are observed. The Social incentives to Encourage Physical Activity and Understand Predictors (STEP UP) trial was a randomized trial comparing multiple interventions that aimed to increase daily step counts among employees at a large professional services company. Using simulations, we propose a multiple imputation framework for estimating optimal decision rules for data with missingness, discuss guidance for reproducible inference, and apply our findings to data from the STEP UP trial.