The adoption of Autonomous Underwater Vehicles (AUVs) to replace manual operations in underwater tasks holds significant potential for marine ranch applications. For continuous operations involving multiple target points in the underwater 3D space, this approach not only enhances operational efficiency but also meets diverse application needs. However, the task allocation and path planning for multiple target points is classified as an NP-hard problem. Additionally, considering the weight of the underwater catch adds further complexity to the optimization of operational energy consumption. In this paper, we take into account the characteristics of the seabed terrain and propose a probability density sampling-based algorithm for obstacle avoidance path planning between target points. Furthermore, we utilize an improved Particle Swarm Optimization (PSO) algorithm to perform task sequencing allocation for a set number of target points, ultimately achieving a bi-objective optimization of total path length and total energy consumption due to payload. This results in an approximate optimal solution. Through simulation experiments, we validate the performance and effectiveness of the proposed algorithm.