To achieve a scientific and efficient emergency response, a dynamic route-planning strategy for emergency vehicles based on government–enterprise collaboration was studied. Firstly, a hybrid evaluation approach was developed, integrating the Analytic Hierarchy Process, Entropy Weight Method, and Gray Relation Analysis-TOPSIS to quantitatively assess the urgency of demands at disaster sites. Secondly, a government–enterprise coordinated route-planning strategy was designed, leveraging the government’s strong mobilizing capabilities and enterprises’ flexible operational mechanisms. Thirdly, to optimize scheduling efficiency, a dynamic route-planning model was proposed, incorporating multiple distribution conditions to minimize scheduling time, delay penalties, and unmet demand rates. A two-stage cellular genetic algorithm was employed to address realistic constraints such as demand splitting, soft time windows, open scheduling, and differentiated services. Numerical simulations of potential flooding in Hunan Province revealed that the collaborative strategy significantly improved performance: the demand satisfaction rate rose from 70.1% (government-led) to 92.3%, while the material transportation time per unit decreased by 23.6% (from 1.61 to 1.23 min/unit). Vehicle path characteristics varied under different operational behaviors, aligning with theoretical expectations. Even under sudden road disruptions, the model maintained a 98% demand satisfaction rate with only a negligible 0.076% increase in system loss. This research fills the gaps in previous studies by comprehensively addressing multiple factors in emergency vehicle route planning, offering a practical and efficient solution for post-disaster emergency response.