Piecewise growth models are very flexible methods for assessing distinct phases of development or progression in longitudinal data. As an extension of these models, this paper presents piecewise growth mixture Tobit models (PGMTMs) for describing phasic changes of individual trajectories over time where the longitudinal data has a mixture of subpopulations and where left censoring due to a lower limit of detection (LOD) is also observed. There has been relatively little work done simultaneously modeling heterogeneous growth trajectories, segmented phases of progression, and left-censoring with skewed responses. The proposed methods are illustrated using real data from an AIDS clinical study. Analysis results suggested two classes of viral load growth trajectories: Class 1 started with a decline in viral load after treatment but rebound after change-point; Class 2 had a decrease the same as the Class 1 and continued a slower decrease over time.