In this work, we are presenting the simultaneous hyperparameter tuning optimization problem for multiple prediction methods under a limited computational time. Configuring the hyperparameters of a given model is an optimization problem with an unknown multidimensional function. Hyperparameter tuning of a multiple prediction methods simultaneously is even a more complex problem. First we explain the motivation for performing simultaneous hyperparameter tuning on multiple prediction models. Then we give an introduction of hyperparameter tuning optimization in general. Then we give a quick review on common optimization approaches. Finally, we explain the different techniques we implemented to tackle this issue. We finish by discussing directions for future research to conduct.