Personalized treatment involves several complex decisions, particularly in the presence of multiple treatment options and continuous dosages. We propose a joint machine learning and optimization framework for treatment prescriptions, in which we leverage ML to learn treatment effects from data and formulate a mixed-integer programming model to identify promising regimens from the ML models. The approach generalizes to multiple treatment objectives and risk tolerances, as well as additional clinically-derived constraints. We demonstrate the method in chemotherapy as well as chronic disease management.