In　noisy　scenes,　speech　enhancement　is　an　important　technology　to　improve　the　speech　quality.　In　this　paper,　a　multi-channel　speech　enhancement　algorithm　with　multiple-target　Generative　Adversarial　Networks　(GANs)　is　proposed.　Firstly,　using　the　spatial　characteristics　of　microphone　array,　the　mask　of　target　speech　signal　is　generated　by　the　multiple-target　GAN　(MT-GAN).　Secondly,　the　mask　is　estimated　based　on　complex　Gaussian　mixture　model　(CGMM),　which　is　combined　with　the　mask　predicted　by　network　in　an　iterative　way　to　obtain　a　more　robust　speech　enhancement　system.　Finally,　the　estimated　mask　is　used　to　construct　beamformer.　Thus,　the　noisy　speech　is　enhanced　by　the　constructed　beamformer.　The　experimental　results　show　that　compared　with　the　reference　methods,　the　speech　quality　and　intelligibility　of　the　proposed　method　are　improved　effectively.　©　2020　IEEE.