Template-based approaches have advanced Event Argument Extraction (EAE) by formulating it as a structured prediction problem. However, existing methods mainly rely on either fixed manually designed templates, which suffer from template under-matching in diverse contexts, or exhaustive enumeration of template permutations, incurring prohibitively high computational cost. Moreover, they often overlook the semantic heterogeneity across events. To address these challenges, we propose ATSR, an Adaptive Template Selection and Role-Aware Routing framework for document-level multi-event EAE, integrating an Adaptive Multi-structure Template Selection (AMSTS) module and a Role-Aware Mixture-of-Experts (RAMoE) module. Specifically, AMSTS leverages a set of pre-defined multi-structure templates for each event type and adaptively selects the most compatible one for each event based on learnable weights conditioned on trigger representations. The RAMoE operates over template-aligned slots and routes each role to specialized experts for event semantic modeling. Extensive experiments on three benchmark datasets demonstrate that ATSR achieves state-of-the-art performance with competitive efficiency, while remaining effective in few-shot settings.