The paper proposes a novel, simple but effective method to align 2D LiDAR laser data. The method uses point-based approach with a simulated annealing searching algorithm. Iterative Closest Point (ICP) is a common method used to solve 2D Lidar alignment problem and widely used to solve Simultaneous localization and mapping (SLAM) problem. The local minima problem allows ICP can be applied only in final aligning steps where data are roughly aligned. The proposed method solves this problem by using simulated annealing (SA) searching algorithm to align two data from distance. SA works on point medium on a point-based approach to reduce the searching dimensions and enhance convergence rate. The method has proved its robustness and efficiency in aligning 2D LiDAR laser data.