Abstract: Information retrieval play an important role in the web. Many research provide many suggestion for retrieve the information to the user in an appropriate way. Linked Open Data (LOD) simplify the information retrieval by focusing on the data rather than the web link and extend the normal web by publishing different data and make an appropriate links between them. The LOD continue growing by adding new web links and that growing make searching the information from it more difficult and need more time. This study proposed an algorithm for using distributed genetic algorithms to improve the information retrieval process in linked open data. Wikipedia is a free online encyclopedia which is created and edited by contributors from many countries around the world. DBpedia which is a web version of DBpedia infer structure information from the wikipedia to make it accessible on the web. In order to facilitate the information retrieval form the DBpedia in the LOD, the suggested algorithm make a clustering for all websites according to their types into seven different clusters and develop a genetic algorithm for each cluster. Steady state Genetic Algorithm with Triple tournament selection is used in each cluster. The replacement strategy was used to improve the total performance of the genetic algorithm by replace the bad individual with a good offspring. Our suggested algorithm give a rise for the importance of using the evolutionary algorithm in linked open data.