The current research aims to evaluate the performance of various approaches for estimating covariates within the latent class membership regression model in the context of growth mixture models. Researchers have been searching for more efficient and accurate estimation methods for incorporating covariate information in mixture modeling in order to clearly differentiate between subjects from different groups and to make interpretation of the growth trajectories more meaningful. However, few studies have considered more complicated models such as growth mixture models where the latent class variable is more difficult to identify. To this end, two Monte Carlo simulations were conducted. In Simulation I, four estimation approaches were investigated to examine parameter recovery, variance and standard error efficacy related to both categorical and continuous covariates that defined the regression model for the latent class membership part of the model. Data generated for Simulation II include three covariates, with one dichotomous variable linked to latent class membership and the other two (one dichotomous and one continuous) associated with measurement part of the growth mixture model. Three estimation approaches were then compared using the population data generation model as well as a misspecified model.