D³G²A is a new multi-agent approach which addresses additive constraint satisfaction problem. This approach is inspired by the guided genetic algorithm （ GGA ） and by the dynamic distributed double guided genetic algorithm for Max_CSPs. It consists of agents dynamically created and cooperating in order to solve problem with each agent performs its own GA. First, our approach is enhanced by three parameters ， guidance probability ， local optima detector ， weight, which allow not only diversification but also escaping from local optima. Second, the GGAs performed agents will no longer be the same. In fact our approach will let the agents able to count their own GA parameters. In order to show D³G²A advantages, the approach and the GGA are applied on the randomly generated binary constraints satisfaction problems. And the result shows that D³G²A is efficient in better fitness values and shorter CPU time