This article considers the problem of finding a route and schedule for a vehicle starting from a depot, visiting a set of customers, and returning to the depot, in a
time-dependent network where the objective is to minimize the greenhouse gas emissions. In this formulation, the speeds of the vehicle as well as the routes chosen are decision variables subject to limits determined by the level of congestion on the roads at the time. Two methods are proposed to find the optimal strategy for a single route. One is a time-increment-based dynamic
programming method, and the other is a new heuristic approach. In addition, a case study is carried out, which compares the performances of these methods, as well as the least polluting routes with the shortest time routes between two customer nodes.