Cellular genetic algorithms (cGAs) are a kind of genetic algorithm (GA) — population based heuristic — with a structured population so that each individual can only interact with its neighbors. The existence of small overlapped neighborhoods in this decentralized population provides both diversity and opportunities for exploration, while the exploitation of the search space is strengthened inside each neighborhood. This balance between intensification and diversification makes cGAs naturally suitable for solving complex problems. In this chapter, we solve a large benchmark (composed of 160 instances) of the Capacitated Vehicle Routing Problem (CVRP) with a cGA hybridized with a problem customized recombination operation, an advanced mutation operator integrating three mutation methods, and two well-known local search algorithms for routing problems. The studied test-suite contains almost every existing instance for CVRP in the literature. In this work, the best-so-far solution is found (or even improved) in 80% of the tested instances (126 out of 160), and in the other cases (20%, i.e. 34 out of 160) the deviation between our best solution and the best-known one is always very low (under 2.90%). Moreover, 9 new best-known solutions have been found.