The Tour Construction Framework (TCF) integrates both global and local heuristics in a complementary framework in order to efficiently solve the Travelling Salesman Problem (TSP). Most tour construction heuristics are strictly local in nature. However, the experimental method presented in this research includes a global heuristic to efficiently solve the TSP. The Global Path (GP) component and Super Node (SN) component comprise the TCF. Each component heuristic is tuned with one or more parameters. Genetic Algorithms (GA) are used to train the collection of parameters for the TCF components on subsets of benchmark TSPs. The GA results are used to run the TCF on the full TSP instances. The performance of the TCF is evaluated for speed, accuracy, and computational complexity, and it is compared against six mainstream TSP solvers: Lin-Kernighan-Helsgaun (LKH-2), 2-Opt, Greedy, Boruvka, Quick-Boruvka, and Nearest Neighbor. The empirical study demonstrates the effectiveness of the TCF in achieving near-optimal solutions for the TSP with reasonable costs.