Graph co-clustering aims to simultaneously group two types of nodes by uncovering patterns within bipartite graphs. While existing co-clustering methods are effective for static bipartite graphs, they are ill-equipped to handle temporal bipartite graphs, where time-ordered edge sequences occur at irregular intervals. This leads to a failure in capturing essential temporal dependencies. To address this, we propose a novel Co-Clustering algorithm for Temporal Bipartite Graphs (ccTBG), designed to uncover temporal patterns and identify co-clusters within these graphs. ccTBG employs distinct generative mixture models for the two types of nodes to model cluster membership and leverages the Hawkes process to capture the temporal patterns of co-clusters from observed edge sequences. Additionally, we develop a variational algorithm for efficient inference and parameter estimation. Extensive experiments on synthetic datasets validate the effectiveness of ccTBG in co-clustering relative to existing methods, while analysis of real-world datasets reveals the discovered co-cluster structures.