Abstract: A data association approach for SLAM is proposed by combining genetic algorithm and joint maximum likelihood (JML) method, named "GAJML" for short. In that approach, "association door" is used for reducing data association solution space to improve search efficiency. Joint maximum likelihood value of data association solution is taken as fitness value. A self-adaptation strategy is implanted into population initialization to improve computation efficiency. Experimental result indicates that its instantaneity is well with only increasing little time-consumption compared with ICNN (individual compatibility nearest neighbor), the data association correct rate is very high even close to that of the JML method, and can effectively overcome "close loop" problem caused localization accumulated error growing.