Effective fusion of medical data from multiple autism research centers contributes to the diagnosis of autism spectrum disorder (ASD), as large multi-site datasets increase the sample size, which facilitates the investigation of the pathological process of ASD. However, the existing methods generally ignore the heterogeneity (i.e., caused by subject populations and different scanning parameters) among diverse data sites, which degrades the effectiveness of model in ASD diagnosis based on multi-site datasets. To address this issue, we propose a novel domain adaptation method for ASD diagnosis based on joint distribution optimal transport (JDOT). Specifically, we alternately treat one site as target domain, and the rest are sources. Afterwards, we perform alignment in source-target domain by seeking a probabilistic coupling between joint feature and label distributions using optimal transport, which is optimized by an alternative minimization approach. Experimental results demonstrate the effectiveness of our method in ASD diagnosis based on multi-site resting-state functional magnetic resonance imaging (rs-fMRI) datasets.