Distributed Genetic Algorithm (DGA) is one of the most promising choices among the optimization methods. In this paper we describe DGAFrame, a flexible framework for evolutionary computation, written in Java. DGAFrame executes GAs across a range of machines communicating through RMI network technology, allowing the implemen- tation of portable, flexible GAs that use the island model approach. Each island can be configured independently from others providing the implementation of heterogeneous DGAs. To evaluate the performance of DGAFrame, we implemented the Protein Structure Prediction problem and compare the DGA execution to its sequential counterpart through quality of solution. We also measure the computa- tion to communication ratio and results show that the pro- posals consistently outperform equivalent sequential GAs.