Multi-constrained Vehicle Routing Problems are gaining steadily in importance. Especially, the dynamic version of the problem has become more emphasis due to modern service requirements, such as short-term or express delivery. With a growing number of dedicated solution approaches for these problems, we investigate a simulation-based supervised learning approach to determine the suitability of a particular algorithm from a set of algorithms for a given dynamic problem instance based on a variety of its characteristics. This decision is known as the Algorithm Selection Problem. We explore the performance space for Greedy and Re-planning algorithms for different dynamic problem instances by simulation and an evolutionary algorithm. For the algorithm selection we test several problem features in combination with two supervised machine learning techniques. The applicability of our approach is demonstrated in a use case for autonomous algorithm selection for Dynamic Vehicle Routing Problem instances.