Routing is a fundamental challenge in civil engineering, with direct implications for the design, planning, and construction of transportation infrastructure such as roads, railways, and urban transit systems. Among various routing-related problems, the Vehicle Routing Problem (VRP) has attracted considerable attention due to its broad applicability in logistics, supply chain management, and service delivery. In the capacitated variant of the VRP (CVRP), each vehicle in the fleet is constrained by a fixed carrying capacity, making the task of determining optimal routes even more complex. Given the combinatorial nature of CVRP, the number of feasible routing configurations grows exponentially with the problem size, which necessitates the use of advanced metaheuristic algorithms capable of efficiently exploring the solution space. This study presents a novel CVRP-solving framework based on the Invasive Weed Optimization (IWO) algorithm, leveraging its strong global search ability, adaptability, and robustness in high-dimensional optimization problems. The proposed method is implemented in MATLAB, and its performance is benchmarked against several well-established optimization approaches. Simulation results reveal that the IWO-based method achieves lower total operational costs and faster convergence rates, demonstrating its potential as a practical decision-support tool for large-scale routing applications in transportation and civil engineering projects.