In the research on drone task allocation, achieving efficient and effective distribution of tasks in complex real-world environments is a critical goal. In heterogeneous drone systems, task allocation becomes more challenging due to differences in the capabilities, performance, and application scopes of individual drones. Additionally, as independent decision-makers, drones may have varying preferences and objectives, further complicating the allocation process. Therefore, it is essential to explore multi-objective optimization methods tailored for heterogeneous drone systems. These methods must account for drone heterogeneity while balancing multiple conflicting goals, such as task completion efficiency, resource utilization, and risk mitigation. By applying multi-objective optimization techniques, decision-makers can evaluate task allocation schemes more comprehensively, identifying optimal solutions that align with the characteristics and preferences of the drones. This chapter focuses on multi-objective optimization methods for task allocation in heterogeneous drones during regional observation missions, analyzes key influencing factors, and designs optimization algorithms to solve the problem.