The use of personal cars presents major environmental challenges in terms of climate change, air pollution and noise pollution the government assumed to solve these objections such as pollution and energy consumption, the multi-modal transport as a solution. In fact, the multi-modal transport ten counters several problems as irritants that are related to the distribution, the condenser of researchers classifies it as a NP-hard problem. The aim of this paper is to establish an enhanced distributed as well as guided genetic algorithm in order to solve the multi-modal transport problem, particularly the problem of disturbance. For that, the solution must be accurate in the normal case and absolutely in the degraded mode too. As a consequence, this study intends to refine the quality of services provided to users. Indeed, our approach is based on evolutionary algorithms, and more specifically on the genetic algorithm. So, we apply hybridization in the selection operator and integration of a new template in the mutation operator supporting a multi-criteria method for the itineraries detection.
