The traditional speech enhancement generative adversarial network (SEGAN) takes the waveform of time-domain speech as the mapping target. When it comes to a low signal-to-noise ratio, the waveform of time-domain speech is drowned in the noise, resulting in a dramatic degradation of the enhancement performance of SEGAN and more serious speech distortion. In response, a multi-stage-time-frequency SEGAN (MS-TFSEGAN) is proposed for speech enhancement. MS-TFSEGAN employs multi-stage generators with dual time-frequency discriminators to continuously refine the mapping results. It captures both time- and frequency-domain information at the same time. In addition, for the further enhancement of learning ability in the frequency domain, MS-TFSEGAN introduces L1 loss in the generator loss function. Experimental results show that the speech quality and intelligibility of MS-TFSEGAN are improved by about 13.32% and 8.97%, respectively, compared with SEGAN under low SNR. A relative improvement of 7.3% in CER is achieved when MS-TFSEGAN is used as the front-end of speech recognition.