Evolution has proven to be an effective method of training heterogeneous multi-agent teams of autonomous agents to explore unknown environments. Autonomous, heterogeneous agents are able to go places where humans are unable to go and perform tasks that would be otherwise dangerous or impossible to complete. However, a serious problem for practical applications of multi-agent teams is how to move from training environments to real-world environments. In particular, if the training environment cannot be made identical to the real-world environment how much will performance suffer. In this research we investigate how differences in training and testing environments affect performance. We find that while in general performance degrades from training to testing, for difficult training environments performance improves in the test environment. Further, we find distinct differences between the performance of different training algorithms with a novel algorithm known as Orthogonal Evolution of Teams producing the best overall performance.