With computers becoming more pervasive, disks becoming cheaper, and sensors becoming ubiquitous, we are collecting data at an ever-increasing pace. However, it is far easier to collect the data than to extract useful information from it. Sophisticated techniques, such as those developed in the multi-disciplinary field of data mining, are increasingly being applied to the analysis of these datasets in commercial and scientific domains. As the problems become larger and more complex, researchers are turning to heuristic techniques to complement existing approaches. This survey chapter examines the role that evolutionary algorithms (EAs) can play in various stages of data mining. We consider data mining as the end-to-end process of finding patterns starting with raw data. The chapter focuses on the topics of feature extraction, feature selection, classification, and clustering, and surveys the state of the art in the application of evolutionary algorithms to these areas. We examine the use of evolutionary algorithms both in isolation and in combination with other algorithms including neural networks, and decision trees. The chapter concludes with a summary of open research problems and opportunities for the future.