Recently, group link prediction has received increasing attention due to its important role in analyzing relationships between individuals and groups. However, most existing group link prediction methods emphasize static settings or only make cursory exploitation of historical information, so they fail to obtain good performance in dynamic applications. To this end, we attempt to solve the group link prediction problem in continuous-time dynamic scenes with fine-grained temporal information. We propose a novel continuous-time group link prediction method CTGLP to capture the patterns of future link formation between individuals and groups. A new graph neural network CTGNN is presented to learn the latent representations of individuals by biasedly aggregating neighborhood information. Moreover, we design an importance-based group modeling function to model the embedding of a group based on its known members. CTGLP eventually learns a probability distribution and predicts the link target. Experimental results on various datasets with and without unseen nodes show that CTGLP outperforms the state-of-the-art methods by 13.4% and 13.2% on average.