Heuristic search is a core area of Artificial Intelligence, successfully applied to planning, constraint satisfaction and game playing. In real-time heuristic search autonomous agents interleave planning and plan execution and access environment locally which make them more suitable for Artificial Life style settings. Over the last two decades a large number of real-time heuristic search algorithms have been manually crafted and evaluated. In this paper we break down several published algorithms into building blocks and then let a simulated evolution re-combine the blocks in a performance-based way. Remarkably, even relatively short evolution runs result in algorithms with state-of-the-art performance. These promising preliminary results open exciting possibilities in the field of real-time heuristic search.