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In recent years, the rapid advancement of smart agriculture has spurred the pursuit of higher real-time scheduling for inter-connected agricultural machinery across multiple regions. This approach aims to achieve more reasonable allocation of agricultural machinery resources. Cross-regional agricultural machinery operations have emerged as the principal service mode for completing the tasks of the “three summer” harvest. Drawing from real-world scenarios of cross-regional wheat harvesting machinery operations, the cross-regional scheduling problem involving multiple depots and machinery types was investigated, incorporating time windows. Economic and environmental costs were simultaneously considered, leading to the establishment of a cross-regional scheduling model with the objective of minimizing scheduling costs. Tailored to the characteristics of the problem, a genetic algorithm variable neighborhood search (GAVNS) was designed. This algorithm enhanced efficiency and flexibility in solution search through operations like crossover, random perturbations, and adaptive neighborhood selection. The operational demands of 72 wheat-producing counties in the Huang-Huai-Hai Plain in China were computed and analyzed. Comparative analysis revealed that the proposed algorithm outperformed alternative algorithms in terms of reduced iteration count to reach the optimal solution and faster convergence speed, with 16.41% decrease compared with the genetic algorithm and 11.15% decrease compared with the variable neighborhood search algorithm in terms of the objective function value. Furthermore, different scheduling modes were compared, highlighting the open path mode as more conducive to enhancing crossregional scheduling service efficiency, leading to 17.76% reduction in scheduling costs compared with the closed path mode.