The Vehicle Routing Problem (VRP) is one of the most studied topics in Operations Research. Among the numerous variants of the VRP, this research addresses the VRP with relaxed priority rules (VRP-RPR) in which customers are assigned to several priority groups and customers with the highest priorities typically need to be served before lower priority ones. Additional rules are used to control the trade-off between priority and cost efficiency. We propose a Mixed Integer Linear Programming (MILP) model to formulate the problem and to solve small-sized instances. A metaheuristic based on the Adaptive Large Neighborhood Search (ALNS) algorithm with problem-tailored components is then designed to handle the problem at larger scales. The experimental results demonstrate the performance of our proposed algorithm. Remarkably, it outperforms a metaheuristic recently proposed to solve the Clustered Traveling Saleman Problem with d-relaxed priority rule (CTSP-d), a special case of VRP-RPR, in both solution quality and computational time.