To address the limitations of traditional optimization algorithms in handling complex optimization problems and to enhance the efficiency of global optimum search, this paper proposes a novel metaheuristic algorithm named the PN Junction Optimization Algorithm (PNOA). Inspired by the motion mechanism of free electrons during the formation of a PN junction, PNOA models free electrons’ diffusion and drift behaviors through a conceptual stage-based framework, which is further formalized using mathematical formulations. Initially, the performance of PNOA was validated on 12 benchmark test functions from the 2022 IEEE Congress on Evolutionary Computation. Additionally, to validate its practicality further, the algorithm is applied to the multi-threshold image segmentation task in a discrete search space. Experimental results demonstrate that PNOA exhibits highly competitive performance in optimization tasks, effectively overcoming the limitations of traditional algorithms and providing a robust and efficient alternative for solving complex optimization problems.