Developing predictive models is one of the key issues in Systems Biology. A critical problems that arises when these models are built is the parameter estimation. The calibration of these nonlinear dynamic models is stated as a nonlinear programming problems (NLP) and its resolution is usually complex due to the frequent ill-conditioning and multimodality of the majority of these problems. For that reason, the use of hybrid stochastic optimization methods has received an increasing interest in recent years. In this work we present a new hybrid method for parameter estimation in Systems Biology. This proposal consists on a set of DE algorithms that cooperate among them through a centralised scheme in which a coordinator controls their behavior by means of a rule system. The comparison with state-of-the-art methods shows the better performance of this cooperative strategy when the complexity of the instances is increased.