Chemotherapy is still one of the most commonly used ways of treating cancer. While being very effective, this method has serious side effects which can harm the healthy parts of the body. To optimize chemotherapy, we need to find a balance between minimizing the tumor volume, and minimizing the injected doses, to reduce harmful side effects. By leveraging current engineering techniques, these objectives can be implemented in a multi-objective optimization process. In this study, we implement two frequently used techniques, namely the NSGA-II (non-dominated sorting genetic algorithm) and the Epsilon-constraint method, to tackle this optimization problem. The outputs of these algorithms are Pareto fronts, which contain all the optimal solutions of the problem, with respect to each objective. To determine their quality, we evaluate the generated Pareto fronts using frequently used metrics. These metrics are the Hypervolume indicator and the Epsilon-metric. According to the results, the Epsilon-constraint method provided better Pareto fronts regarding both examined metrics. The generated Pareto fronts could provide a starting point for the decision-makers in the future in determining the most efficient and cost-effective therapy.