The multi-batch informed trees (MBIT*) algorithm is proposed for mobile robot path planning. The algorithm integrates multiple informed subset generation and path optimization to reduce path planning time and path length. First, the generalized voronoi diagram (GVD) is utilized to generate a heuristic initial collision-free reference path. Then, a multi-informed subset exploration method is constructed to reduce the sampling range and improve the convergence efficiency based on batch informed trees (BIT*) and the reference path. On this basis, to overcome the problem of uneven distribution of sampling points in existing BIT* algorithm, a biased Gaussian sampling strategy based on the distribution of obstacles and the informed subset is leveraged to obtain the optimal path in narrow environment. The theoretical analyses confirm that the proposed algorithm exhibits probabilistic completeness and asymptotic optimality, while also maintaining measurable computational complexity and storage requirements. Furthermore, MBIT* has been developed as a package to be integrated in the robot operating system (ROS). To further validate its effectiveness, simulation studies and performance comparisons with other prevalent sampling-based path planning algorithms were conducted in typical map scenarios. Furthermore, the real-world experiments under identical environmental conditions were carried out. Results indicate that the proposed algorithm offers obvious advantages in terms of path length and planning time, and is feasible for implementation.