Group formation is a critical factor which influences collaborative processes and performances in computer-supported collaborative learning (CSCL). Automatic grouping has been widely used to generate groups with heterogeneous attributes and to maximize the diversity of students' characteristics within a group. But there are two dominant challenges that automatic grouping methods need to address, namely the barriers of uneven group size problem, and the inaccessibility of student characteristics. This research proposes an optimized, genetic algorithm-based grouping method that includes a conceptual model and an algorithm module to address these challenges. Through a quasi-experiment research, we compare collaborative groups' performance, processes, and perceptions in China's higher education. The results indicate that the experimental groups outperform the traditional grouping methods (i.e., random groups and student-formed groups) in terms of final performances, collaborative processes, and student perceptions. Based on the results, we propose implications for implementation of automatic grouping methods, and the use of collaborative analytics methods in CSCL.