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Intelligent transportation systems face various challenges, including traffic congestion, environmental pollution, and inefficient transportation management. Optimizing routes and schedules for efficient delivery of goods and services can mitigate the aforementioned problems. Many transportation and routing problems can be modeled as variants of the Traveling Salesmen Problem (TSP) depending on the specific requirements of the scenario at hand. This means that to efficiently solve the routing problem, all locations have to be visited by the available salesmen in a way that minimizes the overall makespan. This becomes a non-trivial problem when the number of salesmen and locations to be visited increases. The problem at hand is modeled as a special TSP variant, called Extended Colored TSP (ECTSP). It has additional constraints when compared to the classical TSP, which further complicates the search for a feasible solution. This work proposes a new metaheuristic approach to efficiently solve the ECTSP. We compare the proposed approach to existing solutions over a series of test instances. The results show a superior performance of our metaheuristic approach with respect to the state of the art, both in terms of solution quality and algorithm's runtime.