This paper proposes a joint transmit beamforming and movable antenna (MA) position optimization approach for cognitive radio (CR) systems to enhance spectrum sharing between primary and secondary communication systems. The goal is to maximize the received signal power at the secondary receiver (SR) while minimizing co-channel interference to multiple primary receivers (PRs). The proposed method leverages the flexibility of MAs to create favorable channel conditions through spatial reconfiguration. Theoretical analysis shows that MAs can achieve maximum-ratio transmission (MRT) to the SR and effective interference mitigation to PRs simultaneously. To solve the optimization problem, an alternating optimization (AO) algorithm is proposed, combining successive convex approximation (SCA) and discrete sampling approaches. The AO algorithm alternates between optimizing the transmit beamforming vector and the MA positions. Numerical results demonstrate that the proposed algorithm outperforms conventional fixed-position antennas (FPAs) and other baseline schemes in terms of received signal-to-noise ratio (SNR) at the SR. The algorithm is shown to be effective in various scenarios, including different transmit region sizes, interference thresholds, and numbers of transmit paths. The results indicate that MAs provide significant performance improvements over FPAs, especially in large transmit regions and when interference constraints are tight. The proposed method offers a promising solution for improving spectrum efficiency in CR systems.This paper proposes a joint transmit beamforming and movable antenna (MA) position optimization approach for cognitive radio (CR) systems to enhance spectrum sharing between primary and secondary communication systems. The goal is to maximize the received signal power at the secondary receiver (SR) while minimizing co-channel interference to multiple primary receivers (PRs). The proposed method leverages the flexibility of MAs to create favorable channel conditions through spatial reconfiguration. Theoretical analysis shows that MAs can achieve maximum-ratio transmission (MRT) to the SR and effective interference mitigation to PRs simultaneously. To solve the optimization problem, an alternating optimization (AO) algorithm is proposed, combining successive convex approximation (SCA) and discrete sampling approaches. The AO algorithm alternates between optimizing the transmit beamforming vector and the MA positions. Numerical results demonstrate that the proposed algorithm outperforms conventional fixed-position antennas (FPAs) and other baseline schemes in terms of received signal-to-noise ratio (SNR) at the SR. The algorithm is shown to be effective in various scenarios, including different transmit region sizes, interference thresholds, and numbers of transmit paths. The results indicate that MAs provide significant performance improvements over FPAs, especially in large transmit regions and when interference constraints are tight. The proposed method offers a promising solution for improving spectrum efficiency in CR systems.