This chapter presents a novel multiobjective evolutionary approach for determining flight plans for a fleet of unmanned aerial vehicles engaged in exploration and surveillance missions. The study addresses the static off-line planning subproblem, to determine flight routes to optimize the explored area and enhance the surveillance of points of interest within the designated zone. Custom variants of well-known multiobjective evolutionary algorithms are proposed for solving the problem, including specific routing-based evolutionary operators that incorporate problem domain knowledge. The approach is tailored for application in low-cost commercial unmanned aerial vehicles. Realistic instances of the exploration/surveillance problem are used for experimental analysis. The computed results demonstrate the effectiveness of the multiobjective evolutionary approaches in generating accurate flight plans, improving over a previous linear aggregation evolutionary method and over a greedy heuristic developed for the problem. The computed improvements over the previous evolutionary method were up to 23.8/27.1% in the exploration/surveillance objectives of the proposed problem. Furthermore, the multiobjective evolutionary approaches improved over the greedy heuristic by more than 40% in the problem objectives. In turn, the NSGA-II-based solutions provided a more balanced covering of the Pareto front of the problem. The findings underscore the potential of the multiobjective evolutionary approach as a promising solution for unmanned aerial vehicle flight planning in exploration and surveillance missions. The presented results highlight its superiority over existing methods, emphasizing its ability to generate efficient and accurate flight plans, thereby enhancing the capabilities of unmanned aerial vehicle fleets in real-world scenarios.