This paper considers a Green Vehicle Routing Problem (GVRP), which includes heavy-duty electric and con-ventional trucks. We develop a new bi-objective programming model defined on a set of vertices, including a depot, a group of customers, and a set of charging stations. The first objective function is the minimization of the total cost of transportation. To meet the growing environmental concerns, we also consider a second objective function which minimizes total Greenhouse Gas (GHG) emissions. To solve the bi-objective problem, we inte-grate three multi-objective solution methods (i.e., weighted-sum, epsilon-constraint, and hybrid methods) with the Adaptive Large Neighborhood Search (ALNS). We thereby generate a set of instances based on real-world lo-cations in the Greater Toronto Area (GTA) and some parts of Ontario in Canada. These instances are then solved by applying the proposed solution methods. The obtained numerical results from the designed experiments revealed that by enhancing the charging power from 90 kW to 350 kW, transportation costs could decrease by up to 5 %. In addition, by doubling the number of stations in the same service area, delivery companies could lower their transportation costs by 2 %. Furthermore, a slight increase (less than 3 %) in transportation costs leads to a remarkable reduction (more than 18 %) in GHG emissions.
