Presently, the distributed Resource Description Framework (RDF) partition the data across several computer nodes. In that, many existing RDF systems results in expensive query evaluation and high start-up cost. To address these issues, a new optimization algorithm: modified Grey Wolf Optimization (GWO) has been developed in this research paper. In conventional GWO algorithm, after finding the best values of , and , stopping criteria is accomplished. In modified GWO algorithm, after finding the best values of , and , the alpha value once again encircles the possible solutions for obtaining an optimal solution. In RDF data, query optimization is a challenging task, which has been effectively handled by modified GWO algorithm. In the experimental phase, modified GWO showed good performance in terms of execution time and memory usage as compared to the existing methodologies: Partial Evaluation and Centralized Assembly (PECA), Partial Evaluation and Distributed Assembly (PEDA), RDF-3X, Graph-Based SPARQL Query Engine (gStore), and Legato on Lehigh University Benchmark (LUBM) 10000 and DOREMUS 2017 datasets. Compared to these existing systems, the proposed system reduced the execution time around 2-5 minutes, and improves the precision, recall, and f-measure around 2-7%.