The crossover and mutation operators are essential for traversing chemical space. One of the main challenges with genetic algorithms is the balance of exploration and exploitation. In each generation, there is a population of candidates, some of which perform much better than others in an optimization task. Considering that the goal of the GA is to find high-performing solutions, we want to optimize the top candidates in each generation by mixing and matching their genes. This crossover step is crucial for exploiting the nearby chemical space. However, if we were to rely on just the crossover operation, the GA can easily get trapped at a local maximum and fail at finding the global best solution. To resolve this, the mutation operation can be performed on the newly formed child, during which one of the child's genes receives a random change. Mutation ensures diversity and introduces new genes into each population. Since we still want to balance exploitation and exploration, each child has a chance of undergoing a mutation. The probability of a mutation occurring is set by the mutation rate, one of the hyperparameters of the GA. Although a wide variety of mutation rates have been reported in the literature , ranging from 1% to 50%, there have been few reports on optimizing this parameter for chemical applications . In this work, we examine mutation rates ranging from 10% to 90% with 10% increments.