This article applies growth curve models to longitudinal count data characterized by an excess of zero counts. We discuss a zero-inflated Poisson regression model for longitudinal data in which the impact of covariates on the initial counts and the rate of change in counts over time is the focus of inference. Basic growth curve models using a single outcome are described, as well as a model in which two linked outcomes constitute a dual-trajectory growth process. This model is applied to assess the impact of changes in financial stress on longitudinal change in smoking.