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Self-driving car automated control problem includes automated routing problem. This paper addresses the self-driving taxi routing problem formalized as the Pickup and Delivery problem (PDP). We use small moves technique as the basis for our evolutionary computation framework that considers small moves as mutation operations. We introduce strategies, which determine vector of probabilities to apply small moves on each step. Also we introduce policies, which determine how strategies evolve during the algorithm runtime. Finally, we also apply meta-learning to select the best strategy and policy for a given dataset. We test suggested methods with several genetic schemes. The best performance is shown by strategies that apply small moves with probability depending on its success rate and/or complexity. It outperforms Hill Climbing as well as a constraint satisfaction problem solver adopted for PDP. [ABSTRACT FROM AUTHOR]