Longitudinal item response data often exhibit two types of measurement noninvariance: the noninvariance of item parameters between subject groups and that of item parameters across multiple time points. This study proposes a comprehensive approach to the simultaneous modeling of both types of measurement noninvariance in terms of longitudinal item response modeling while incorporating a latent growth curve model. The growth curve model represents the growth trajectory using initial status and (non)linear change over multiple time points. In addition, this study investigates the impact of measurement noninvariance across time points and subject groups in the examination of group differences in growth over multiple time points. To demonstrate the efficacy of the proposed model, an empirical application to large-scale longitudinal survey data is provided, along with the results of a simulation study.