In automatic parking scenarios, obstacles on both sides of a target parking space (TPS) will degrade the feasibility and comfort of passengers boarding and alighting (CPBA) when the target vehicle maintains a standard final parking state (FPS). Additionally, the presence of dynamic obstacle vehicles (DOV) in the parking lot severely challenges the adaptability of path - planning algorithms. To tackle these issues, this paper presents a method for FPS optimization and two-stage path planning in dynamic scenarios while considering CPBA. First, a CPBA characterization variable is established using the allowed opening angle of the vehicle door (OAVD) restricted by adjacent obstacles. Then, to enhance path quality, the basic motion curve (BMC) of the vehicle is formulated for the design of queuing, parking, and transition paths. A two-stage path planning framework that integrates the parking lot system and the automatic parking system (APS) of the target vehicle is proposed. It includes a Dijkstra algorithm with embedded occupancy dynamic time windows of corridor nodes (DDTW) for queuing path planning and a parking path planning method based on the multi-objective particle swarm optimization (MOPSO) algorithm, aiming to improve CPBA and parking efficiency. Simulation experiments using real-world parking lot and vehicle parameters show that the proposed method can effectively reduce queuing time and enhance comfort, validating its effectiveness. A comparison with other parking path planning methods further demonstrates the superiority of the path performance achieved by the proposed approach.