Correctly tuning the hyperparameters of a machine learning model can improve classification results. Typically hyperparameter tuning is made by humans and experience is needed to fine tune them. Algorithmic approaches have been extensively studied in the literature and can find better results. In our work we employ a quantum genetic algorithm to address the hyperparameter optimization problem. The algorithm is based on qudits instead of qubits, allowing more available states. Experiments were performed on two datasets MNIST and CIFAR10 and results were compared against classic genetic algorithms.