Due to the ubiquitous nature and anonymity abuses in cyberspace, it's difficult to make criminal identity tracing in cybercrime investigation. Writeprint identification offers a valuable tool to counter anonymity by applying stylometric analysis technique to help identify individuals based on textual traces. In this study, a framework for online writeprint identification is proposed. Variable length character n-gram is used to represent the author's writing style. The technique of IG seeded GA based feature selection for Ensemble (IGAE) is also developed to build an identification model based on individual author level features. Several specific components for dealing with the individual feature set are integrated to improve the performance.