Factor mixture modeling (FMM) is generally complex with both unobserved categorical and unobserved continuous variables. We explore the potential of item parceling to reduce the model complexity of FMM and improve convergence and class enumeration accordingly. To this end, we conduct Monte Carlo simulations with three types of data, continuous, polytomous, and binary under two levels of model complexity, constrained FMM under strict invariance and relaxed FMM under scalar or metric invariance. The results show that item parceling could be advantageous for FMM with binary items but not with continuous or polytomous items.