This paper addresses the issue of correcting type errors in model transformations in realistic scenarios where neither predefined patches nor behavior-safe guards such as test suites are available. Instead of using predefined patches targeting isolated errors of specific categories, we propose to explore the space of possible patches by combining basic edit operations for model transformation programs. To guide the search, we define two families of objectives: one to limit the number of type errors and the other to preserve the transformation behavior. To approximate the latter, we study two objectives: minimizing the number of changes and keeping the changes local. Additionally, we define four heuristics to refine candidate patches to increase the likelihood of correcting type errors while preserving the transformation behavior. We implemented our approach for the ATL language using the evolutionary algorithm NSGA-II, and performed an evaluation based on three published case studies. The evaluation results show that our approach was able to automatically correct on average more than82% of type errors for two cases and more than 56% for the third case.