In this paper, the authors investigate a novel approach for partitional clustering of a large collection of text documents by using an improved version of the classical Differential Algorithm (DE). Fast and accurate clustering of documents plays an important role in the field of text mining and automatic information retrieval systems. The k-means has served as the most widely used partitional clustering algorithm for text documents. However, in most cases it provides only locally optimal solutions. In this paper, the clustering problem has been formulated as an optimization task and is solved using a modified DE algorithm.