To address the issues of cumbersome preparation, high costs, and low efficiency in antenna near-field measurements, this paper proposes a method for waypoint selection and path planning using unmanned aerial vehicles (UAVs) for the measurement area, aiming to improve the efficiency of antenna near-field measurements. First, an Electromagnetic-induction-based Adaptive Sampling strategy (EAS) is employed to select waypoints on the measurement plane. The electromagnetic field propagation principle is used to construct an electromagnetic distribution matrix, followed by a dynamic threshold adjustment strategy to filter the waypoints that meet the required conditions. Secondly, this paper introduces an improved Arctic Puffin Optimization algorithm based on tabu search, called the Tabu Search-Arctic Puffin Optimization algorithm (TS-APO), for path planning of the selected waypoints. Compared to traditional antenna near-field measurement methods, the proposed approach effectively reduces the number of measurement points and the scanning path length. Experimental results comparing with similar path planning algorithms show that the TS-APO algorithm shortens planning time while maintaining strong optimization capability. To meet the UAV's flight dynamics constraints, a piecewise uniform B-spline curve method is applied to smooth the generated path. UAV software-in-the-loop simulations using AirSim validate the feasibility of the algorithm. Data validation with a 3-m parabolic antenna operating at 1 GHz demonstrates that the proposed method can significantly improve measurement efficiency while ensuring the accuracy of antenna near-field measurements.