Joint modeling of longitudinal and time-to-event data provides insights into the association between the two quantities. The joint latent class modeling approach assumes that conditioning on latent class membership, the trajectories of longitudinal data are independent of survival risks. The most common parametric approach, the joint latent class model (JLCM), suffers from high computational cost and restricts analysis to using time-invariant covariates in modeling survival risks and latent class memberships. We propose a nonparametric joint latent class modeling approach based on trees (JLCT), which is fast to fit and can use time-varying covariates in all of its modeling components. We compare JLCT with JLCM on simulated data, where we show that JLCT and JLCM have similar performance when using only time-invariant covariates. Further, we demonstrate the prognostic value of using time-varying covariates in each of the modeling components, and thus display the advantage of JLCT when making predictions. We further apply JLCT to the PAQUID dataset, and demonstrate again that JLCT admits competitive prediction performance, while being orders of magnitude faster than JLCM.