While cancer has traditionally been considered a genetic disease, mounting evidence indicates an important role for non-genetic (epigenetic) mechanisms. Common anti-cancer drugs have recently been observed to induce the adoption of non-genetic drug-tolerant cell states, thereby accelerating the evolution of drug resistance. This confounds conventional high-dose treatment strategies aimed at maximal tumor reduction, since high doses can simultaneously promote non-genetic resistance. In this talk, we propose a mathematical model of drug-induced cell plasticity, and we use this model to study optimal drug scheduling. We show that the optimal dosing strategy steers the tumor into a fixed equilibrium composition between drug-sensitive and drug-tolerant cells, while precisely balancing the trade-off between sensitive cell kill and tolerance induction. The optimal equilibrium strategy ranges from applying a low dose continuously to applying the maximum dose intermittently, depending on the dynamics of tolerance induction. We put our findings within the context of personalized medicine, where we envision a pipeline integrating mathematical modeling with experiments involving in vitro models of patient tumors, to ultimately achieve individualized model-informed dosing strategies.