Simultaneous segmentation and full quantification (estimation of all diagnostic indices) of the myocardial infarction (MI) area are crucial for early diagnosis and surgical planning. Current clinical methods still suffer from high-risk, non-reproducibility and time-consumption issues. In this study, the multitask generative adversarial networks (MuTGAN) is proposed as a contrast-free, stable and automatic clinical tool to segment and quantify MIs simultaneously. MuTGAN consists of generator and discriminator modules and is implemented by three seamless connected networks: spatio-temporal feature extraction network comprehensively learns the morphology and kinematic abnormalities of the left ventricle through a novel three-dimensional successive convolution; joint feature learning network learns the complementarity between segmentation and quantification through innovative inter-and intra-skip connection; task relatedness network learns the intrinsic pattern between tasks to increase the accuracy of estimations through creatively utilized adversarial learning. MuTGAN minimizes a generalized divergence to directly optimize the distribution of estimations by using the competition process, which achieves pixel segmentation and full quantification of MIs. Our proposed method yielded a pixel classification accuracy of 96.46%, and the mean absolute error of the MI centroid was 0.977 mm, from 140 clinical subjects. These results indicate the potential of our proposed method in aiding standardized MI assessments.