Algorithm Selection is a problem that involves finding a way to select the best algorithm out of a portfolio of candidate algorithms, depending on a set of instances for a problem. It has been shown that optimal decision trees that work on binary features are as accurate as state of the art models like random forest, while being more interpretable and smaller, motivating research into alternative, more scalable methods to generate such trees. In this paper we present an optimal decision tree algorithm that operates directly on continuous features and measure its suitability for the algorithm selection problem. We show that our algorithm performs over three orders of magnitude better than other algorithms that build similar decision trees, and that it achieves similar out of sample model selection quality as state of the art methods while being at least 2x faster than similar methods for binary features for higher binarization values.