This article presents a new evolutionary algorithm (EA) for induction of mixed decision trees. In nonterminal nodes of a mixed tree, different types of tests can be placed, ranging from a typical inequality test up to an oblique test based on a splitting hyper-plane. In contrast to classical top-down methods, the proposed system searches for an optimal tree in a global manner, that is it learns a tree structure and finds tests in one run of the EA. Specialized genetic operators are developed, which allow the system to exchange parts of trees, generating new sub-trees, pruning existing ones as well as changing the node type and the tests. An informed mutation application scheme is introduced and the number of unprofitable modifications is reduced. The proposed approach is experimentally verified on both artificial and real-life data and the results are promising. Scaling of system performance with increasing training data size was also investigated.