Nowadays, search engines tend to use the latest technologies in enhancing the personalization of web searches, which leads to a better understanding of user needs. One of these technologies is web search results clustering which returns meaningful labeled clusters from a set of Web snippets retrieved from any Web search engine for a given user’s query. Search result clustering aims to improve searching for information from the potentially huge amount of search results. These search results consist of URLs, titles, and snippets (descriptions or summaries) of web pages. Dealing with search results is considered as treating large-scale data, which indeed has a significant impact on effectiveness and efficiency. However, unlike traditional text mining, queries and snippets tend to be shorter which leads to more ambiguity. K-means tend to converge to local optima and depend on the initial value of cluster centers. In the past, many heuristic algorithms have been introduced to overcome this local optima problem. Nevertheless, these algorithms suffer several shortcomings. In this paper, we present an efficient hybrid web search results clustering algorithm referred to as G-K-M, whereby, we combine K-means with a modified genetic algorithm. The AOL standard dataset is used for evaluating web data log clustering. ODP-239 and MORESQUE are used as the main gold standards for the evaluation of search results clustering algorithms. The experimental results show that the proposed approach demonstrates its significant advantages over traditional clustering. Besides, results show that proposed methods are promising approaches that can make search results more understandable to the users and yield promising benefits in terms of personalization.