Longitudinal randomized controlled trials generally assign individuals randomly to interventions at baseline and then evaluate how differential average treatment effects evolve over time. This study shows that longitudinal settings could benefit from "Recurrent Individual Treatment Assignment" ("RITA") instead, particularly in the face of (dynamic) heterogeneous treatment effects. Focusing on the optimization of treatment assignment, rather than on estimating treatment effects, acknowledges the presence of unobserved heterogeneous treatment effects and improves overall intervention response when compared to intervention policies in longitudinal settings based on "Randomized Controlled Trials" ("RCTs")-derived average treatment effects. This study develops a "RITA"-algorithm and evaluates its performance in a multi-period simulation setting, considering two alternative interventions and varying the extent of unobserved heterogeneity in individual treatment response. The results show that "RITA" learns quickly, and adapts individual assignments effectively. If treatment heterogeneity exists, the inherent focus on both exploit and explore enables "RITA" to outperform a conventional assignment strategy that relies on "RCT"-derived average treatment effects.