<p>Advanced cancer treatment requires the collaboration of highly specialized medical experts in a chain of information-intensive strategies; one way to alleviate this burden and better inform future trials is to build reliable models for drug administration. We design a dynamic model that incorporates both treatment-enhancing optimization and relaxation session scheduling based on model parameters to aid in drug delivery. A control strategy was devised in each treatment phase to optimize drug delivery, while metaheuristic algorithms were used to determine the length of each treatment and relaxation phase. Through several simulation experiments, we show that our framework’s treatment phases balance the risk of local tumor recurrence and chemotherapy-induced side effects better than current practices, shortening the treatment period and reducing side effects. Our study adds to the literature by improving healthcare delivery practices through the development of useful therapy schedules alternating between treatment and relaxation to enable cells to recover from the adverse effects of chemotherapy.</p>