Traffic congestion problems are very common in large municipalities, especially in areas with karst features. Traffic jams happen in many key traffic nodes (such as the bridges across the river and the tunnels through the mountains) frequently, which may lead to severe challenges for the vehicle routing optimization. To effectively solve the routing problem of Cash-in-Transit (CIT) sectors, this study aims to establish a novel bi-objective Cash-in-Transit Vehicle Routing Problem (CTVRP) model, including both the economic and environmental objectives, and designs a Nearest Neighbor-first Iterated Local Search-second algorithm (NN-ILS) with the consideration of the special terrain. Then, a case study of a CIT company is performed to demonstrate the model and algorithm and a vivid solution is presented in real road network after the optimization by using the route fitting procedure. Meanwhile, the accuracy and effectiveness of the algorithm is verified by comparing it with several classical algorithms and OR-Tools solver. The experimental results show that the developed algorithm can help the decision-makers to obtain the solutions with high quality compared with the classical algorithms. Finally, the uncertainty of the developed algorithm is analyzed empirically and the Multi-Attribute Decision Making (MADM) combined with Principal Component Analysis (PCA) is utilized to support decision-makers to select the best satisfying solution instead of choosing the solution with minimum objective value(s).