In this talk, nonparametric methods for multivariate growth curve data are presented. In the semi-parametric situation where mean-based inference is sought, parametric and nonparametric bootstraps are known to have satisfactory finite sample performance in the general factorial designs. In this regard, our aim is to provide a resampling-based tests for multivariate growth curve data that are useful in the situations where the data is not necessarily exchangeable under the null hypothesis of interest. In some studies, the outcome of interest may not be amenable for a mean-based inference. For example, in studies that target management of chronic disease, Quality of Life (QoL) outcomes measured in order-categorical scales are used. For these situations, we propose a resampling-based fully nonparametric procedures. Simulation studies are conducted to evaluate the finite sample performance of the proposed procedures under various practical scenarios. Data from a Pediatric Asthma Prevention Study and an optometry study will be used to illustrate the benefits of the nonparametric methods proposed.