To solve the maximum power point tracking (MPPT) problem of a hybrid photovoltaic-thermoelectric generator (PV-TEG) system to improve energy conversion efficiency and utilization, a hybrid PV-TEG system MPPT technique based on an exponential distribution optimizer (EDO) algorithm is proposed. The EDO searches the potential solution space by modeling random variations of the exponential distribution. Because of the randomness, the algorithm can effectively avoid falling into a local optimum under partial shading condition (PSC), and explores extensively in the search space to find the optimal solution. The case study includes four parts: start-up test, step change of solar irradiance, stochastic variation, and actual cases of four seasons in Hong Kong, and is compared to and analyzed with five other algorithms to more comprehensively verify the feasibility and effectiveness of the proposed EDO technique in the application of hybrid system MPPT. The simulation results show that the hybrid PV-TEG system with EDO can achieve superior MPPT performance stably and efficiently in different operating conditions. In particular, the energy generated by EDO in low irradiance conditions in spring exceeds 68.85%, 66.13%, and 59.69% of the energy outputs of the dragonfly algorithm (DA), incremental conductance method (INC), and perturbation observation method (P&O), respectively.