Trajectory planning is a fundamental requirement for autonomous quadrotor navigation, particularly in obstacle-constrained environments where safety, efficiency, and reliability must be optimized simultaneously. This study compares the performance of six metaheuristic optimization algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO), for quadrotor trajectory planning using a Python-based simulation framework. A two-dimensional mission environment containing multiple static obstacles was developed, and each algorithm was employed to generate collision-free trajectories between predefined start and target locations. Performance evaluation was conducted using path length, energy consumption, computational time, trajectory smoothness, obstacle clearance distance, and mission success rate. Each algorithm was executed for 30 independent simulation runs to ensure robustness and consistency of results. Among the evaluated approaches, GWO achieved the shortest mean path length (126.6 ± 2.4 m), lowest energy consumption (5.09 ± 0.13 kJ), highest obstacle clearance, and a 100% mission success rate, demonstrating superior overall performance. WOA and PSO also exhibited competitive results, while GA and ACO showed comparatively lower optimization efficiency. The findings indicate that swarm-based optimization algorithms, particularly GWO, are highly effective for quadrotor trajectory planning and provide a promising framework for intelligent autonomous aerial navigation systems.