This paper introduces DAFO, a Distributed Agent Framework for Optimization that helps in designing and applying Coevolutionary Genetic Algorithms (CGAs). CGAs have already proven to be efficient in solving hard optimization problems, however they have not been considered in the existing agent-based metaheuristics frameworks that currently provide limited organization models. As a solution, DAFO includes a complete organization and reorganization model, Multi-Agent System for EVolutionary Optimization (MAS4EVO), that permits to formalize CGAs structure, interactions and adaptation. Examples of existing and original CGAs modeled using MAS4EVO are provided and an experimental proof of their efficiency is given on an emergent topology control problem in mobile hybrid ad hoc networks called the injection network problem.