Efficient scheduling of electric Vertical Take-off and Landing (eVTOL) aircraft is regarded as the key to high-quality low-altitude traffic. However, incomplete constraint models are found in current scheduling research. Additionally, local optima issues are easily encountered by traditional algorithms. Therefore, a multi-objective scheduling method regarding energy consumption and delay is investigated in this paper for controlled low-altitude scenarios. First, an eVTOL scheduling model was constructed. Multi-dimensional constraints were included, such as task spatio-temporal factors, resource allocation, sequence relationships, and conflict avoidance. Second, an improved multi-objective genetic algorithm was proposed. Reinforcement learning and Tabu search were integrated into the algorithm. The problem complexity was reduced by a task string preprocessing strategy. A parameter adaptive strategy based on Q-learning was introduced to adjust crossover and mutation rates dynamically. Meanwhile, a local Tabu search strategy based on critical task strings was designed. The ability to escape local optima was enhanced by this strategy. Finally, simulation scenarios are constructed. The effectiveness of the key strategies and the overall algorithm is verified through experiments. Compared with the traditional NSGA-II algorithm, an improvement of 36.2% is achieved in the Inverted Generational Distance (IGD) metric. Additionally, an improvement of 25.6% is obtained in the Hypervolume (HV) metric. A reference is provided by these research results for the optimization of low-altitude traffic scheduling systems.