In disaster relief missions, the timely and reliable transportation of emergency supplies is of critical importance. However, conducting aerial delivery operations in complex plateau regions presents a series of formidable challenges, such as dramatic terrain undulations and harsh meteorological conditions. These factors often hinder the performance of traditional path planning algorithms for convergence velocity, global optimization capability, and path stability, ultimately compromising the safety as well as efficiency of drone flights. To tackle these difficulties, this work proposes a multi-strategy improved polar lights optimizer (MSPLO) aimed at enhancing the path planning performance of logistics drones operating in complex plateau terrain. The proposed algorithm integrates two key strategies: Aurora Particle Cooperative Adaptive Search (ACAS) and Excited Dispersion Search (EDS). Specifically, the ACAS strategy, in conjunction with fundamental search rules, improves the algorithm's global exploration capability and enhances population diversity, enabling the generation of a broader and more feasible set of flight paths that can better accommodate various constraints. Meanwhile, the EDS strategy further diversifies the population and mitigates the risk of premature convergence. To validate the effectiveness of MSPLO, three experimental scenarios with varying scales and complexities were constructed using real-world digital elevation model (DEM) data for plateau regions. Experimental results demonstrate that MSPLO outperforms comparative algorithms in minimizing flight cost, thereby confirming its practical utility and effectiveness in high-altitude emergency transportation scenarios.