Event detection refers to the task of identifying event occurrences in a given natural language text. Event detection comprises two subtasks; recognizing event mention (event identification) and the type of event (event classification). Breaking from the sequence labeling and word classification approaches, this work models event detection, and its constituent subtasks of trigger identification and trigger classification, as independent sequence generation tasks. This work proposes a prompted multi-task generative model trained on event identification, classification, and combined event detection. The model is evaluated on on general-domain and biomedical-domain event detection datasets, achieving state-of-the-art results on the general-domain Roles Across Multiple Sentences (RAMS) dataset, establishing event detection benchmark performance on WikiEvents, and achieving competitive performance on the general-domain Massive Event Detection (MAVEN) dataset and the biomedical-domain Multi-Level Event Extraction (MLEE) dataset.