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Aiming to address the issues of low planning efficiency and long planning paths of apple-picking robotic arms in unstructured orchard environments, a target-guided multi-objective apple picking path planning method (BD-PSO_TG-RRT*) was proposed, which combined a particle swarm optimization (PSO) algorithm incorporating a branch density parameter with a target-guided rapidly-exploring random tree star (TG-RRT*) path planning algorithm. Firstly, based on the traditional RRT* algorithm, an adaptive step-size strategy was introduced, and an equilateral conical sampling region was defined. A target-biasing strategy was also incorporated to enhance the goal-directedness of sampling within this region. A direct connection strategy was used for new nodes to enable faster convergence, thereby improving the speed of path generation. Secondly, the initial planned path was refined by removing redundant points and transforming it into a smooth path using cubic B-spline curves, improving path quality. Lastly, to account for obstacles such as branches during the picking process, a branch density parameter was introduced into the PSO algorithm to obtain the optimal solution for the multi-objective picking sequence. Experimental results for path planning showed that compared with the RRT and RRT* algorithms, the TG-RRT* algorithm reduced average path length by 23.18% and 11.67%, respectively, decreased average time by 12.59% and 71.96%, and lowered the average number of iterations by 68.07% and 31.58%. In multi-objective picking experiments, the BD-PSO_TG-RRT* algorithm with the branch density parameter reduced average planning time by 8.14% and average iterations by 13.24% compared with the original PSO combined with TG-RRT* algorithm. These experimental results demonstrated that the BD-PSO_TG-RRT* algorithm accurately generated an optimal path for multi-objective applepicking, shortened the path length, reduced planning time, and significantly improved the efficiency of multi-objective apple-picking path planning. This algorithm can provide technical reference for apple picking robots to perform multi-objective continuous picking tasks.