According to the American Cancer Society, each year around 1.6 million new cancer cases are diagnosed in the United States. More than half of all cancer patients are treated using radiotherapy. The goal of radiotherapy is to deliver sufficient radiation dose to the tumor region while sparing the surrounding healthy tissues to the largest extent possible. To achieve this goal, mathematical optimization techniques have been traditionally used to sequentially find the optimal settings of the radiation beams as well as the number and content of treatment sessions for each individual cancer patient. Using a biological dose response model, this research integrates the two stages into a single optimization problem in order to investigate the potential therapeutic gain obtained by changing the beam settings over the course of the treatment. The proposed approach is applied to a phantom cancer case to test its computational performance and to quantify the therapeutic gain.