In modern clinical research, there is a growing interest in studying heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allows additional information to be incorporated into the clustering process, which generates deeper biological insight and clinical significance. In the current study, we propose a Bayesian consensus clustering model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows different markers to have local clusters and these local clusters adhere to a global cluster via an adherence function. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. Results of analyzing the real and simulated data will be presented and discussed.