In this paper we present an approach based on the application of an evolutionary algorithm to optimally tune the parameters of a novel technique for effective Web information retrieval. Context matching is a context-based technique for the ad-hoc retrieval of Web documents that relies on a number of inter-related parameters that define the nature of the context it uses. Its aim is to dynamically generate a context-based measure of term significance during retrieval that can be used as an indicator of document relevancy and ultimately contribute to a documents rank score. But the optimal setting of context matching parameters is an important aspect of the technique to ensure effective retrieval. Thus, the goal of this paper is to investigate the use of an evolutionary algorithm for the optimization of context matching parameters and compare its performance to an iterative technique that exhaustively explores combinations of parameters. We show how the most effective settings for parameters are obtained efficiently through the evolutionary algorithm. We also show how context matching, through the use of these optimized parameters, achieves effective retrieval results on benchmark data that are a significant improvement on previously published results.