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Multiple sequence alignment is the base of a growing number of Bioinformatics applications. This does not mean that the accuracy of the existing methods corresponds to biologically faultless alignments. Searching for the optimal alignment for a set of sequences is often hindered by the size and complexity of the search space. Parallel Genetic Algorithms are a class of stochastic algorithms which can increase the speed up of the algorithms. They also enhance the efficiency of the search and the robustness of the solutions by delivering results that are better than those provided by the sum of several sequential Genetic Algorithms. AlineaGA is an evolutionary method for solving protein multiple sequence alignment. It uses a Genetic Algorithm on which some of its genetic operators embed a simple local search optimization. We have implemented its parallel version which we now present. Comparing with its sequential version we have observed an improvement in the search for the best solution. We have also compared its performance with ClustalW2 and T-Coffee, observing that Parallel AlineaGA can lead the search for better solutions for the majority of the datasets in study.