At each generation of an ant algorithm, each ant builds a solution step by step by adding an element to it. Each choice is based on the greedy force (short term profit or heuristic information) and the trail system (central memory which collects information during the search process). Usually, all the ants of the population have the same char- acteristics and behaviors. In contrast in this paper, a new type of ant metaheuristic is proposed. It relies on the use of ants with different per- sonalities. Such a method has been adapted to the well-known vehicle routing problem, and the obtained average results are very encouraging. On one benchmark instance, new best results have been found.