We show how agent-based simulation is used for analyzing different queuing strategies in the youth health care sector. The simulation model represents an authentic business case and is parameterized with actual market data. We discuss the differences between four queuing strategies which are based on push/pull allocation and centralized/decentralized queuing strategies. The model incorporates, among others, a withdrawal and return mechanism, a non-stationary Poisson arrival process, and a preference algorithm to include a care provider’s case preference. The investigated system accommodates extensive waiting lines which are currently solely judged on their length. We have identified that performance measurement in youth health care should not be focused on queue lengths alone, but should include a case difficulty parameter as well. The simulation results, together with contextual data obtained from stakeholder interviews, indicate that a push strategy with a centralized queue suites the sector best. Most related research in health care focuses on queuing theory which fails to address the complexity of the case. Our simulation approach incorporates additional complexities of the case at hand which turn out to be relevant for the queuing strategy decision. We validate the model and strategies by comparison with real market data and field expert discussions.