This work studies the suitability of state-of-the art continuous optimization evolutionary algorithms for designing combination cancer chemotherapies. The design of cancer chemotherapy treatments can be formulated as an optimization problem. Specifically as an optimal control problem, where the underlying dynamical system models the tumor progression; and the controls are the drug concentrations delivered into the system through time. The objective is to minimize the tumor levels during and at the end of the treatment, while maintaining the patient health at an acceptable level. Efficient optimization algorithms are required as modeling the cancer progression and its treatment is computationally expensive. We compare three state-of-the-art evolutionary algorithms for numerical optimization, namely, CMA evolution strategy,differential evolution, and particle swarm optimization. The comparison considers algorithm's convergenceproperties, the quality, and the diversity of the obtained solutions. The underlying mathematical model of tumor growth incorporates tumor cells in different phases of the cell cycle, and the immune system response. Moreover two types of cancer drugs are modeled: a cytotoxic agent and a cytostatic agent.