Orthognathic surgery demands precise preoperative planning to achieve optimal functional and aesthetic results, yet current practices remain labor-intensive and highly dependent on surgical expertise. To address these challenge, we propose OrthoPlanner, a novel two-stage framework for automated orthognathic surgical planning. In the first stage, we develop JawFormer, a shape sensitive transformer network that predicts postoperative bone morphology directly from preoperative 3D point cloud data. Built upon a point cloud encoder-decoder architecture, the network integrates anatomical priors through a region-based feature alignment module. This enables precise modeling of structural changes while preserving critical anatomical features. In the second stage, we introduce a symmetry-constrained rigid alignment algorithm that automatically outputs the precise translation and rotation of each osteotomized bone segment required to match the predicted morphology. This ensures bilateral anatomical consistency and facilitates interpretable surgical plans. Compared with existing approaches, our method achieves superior quantitative performance and enhanced visualization results, as demonstrated by 65 experiments on real clinical datasets. Moreover, OrthoPlanner significantly reduces planning time and manual workload, while ensuring reproducible and clinically acceptable outcomes.