Eliminating all stop words from the feature space is a standard practice of preprocessing in text mining, regardless of the domain which it is applied to. However, this may result in loss of important information, which adversely affects the accuracy of the text mining algorithm. Therefore, this paper proposes a novel methodology for selecting the optimal set of domain specific stop words for improved text mining accuracy. First, the presented methodology retains all the stop words in the text preprocessing phase. Then, an evolutionary technique is used to extract the optimal set of stop words that result in the best classification accuracy. The presented methodology was implemented on a corpus of open source news articles related to critical infrastructure hazards. The first step of mining geo-dependencies among critical infrastructures from text is text classification. In order to achieve this, article content was classified into two classes: 1) text content with geo-location information, and 2) text content without geo-location information. Classification accuracy presented methodology was compared to accuracies of four other test cases. Experimental results with 10-fold cross validation showed that the presented method yielded an increase of 1.76% or higher in True Positive (TP) rate and a 2.27% or higher increase in the True Negative (TN) rate compared to the other techniques.