In dynamic optimization problems, changes occur over time. These changes could be related to the optimization objective, the problem instance, or involve problem constraints. In most cases, they are seen as an ordered sequence of sub-problems or environments that must be solved during a certain time interval. The usual approaches tend to solve each sub-problem when a change happens, dealing always with one single environment at each time instant. In this paper, we propose a multi-environmental cooperative model for parallel meta-heuristics to tackle dynamic optimization problems. It consists in dealing with different environments at the same time, using different algorithms that exchange information coming from these environments. A parallel multi-swarm approach is presented for solving the Dynamic Vehicle Routing Problem. The effectiveness of the proposed approach is tested on a well-known set of benchmarks, and compared with other meta-heuristics from the literature. Experimental results show that our multi-environmental approach outperforms conventional meta-heuristics on this problem.