It is important to generate response plan to deal with the emergency, which will greatly decrease the cost. The traditional method is using the expert system (rule based reasoning system) to generate the decision method. However, this approach is often slow and it is also hard to generate the reasoning rules sometimes. This paper combines two kinds of artificial intelligence techniques, case based reasoning (CBR) and rule based reasoning (RBR), to construct a quick emergency response plan generation system. It improves the performance and solves the knowledge acquisition bottleneck of traditional RBR systems. With the CBR tool CbrSys, decision support is generated from the previous emergency cases and solutions in database through the similarity retrieval. Once new emergency events happen, the case base is first retrieved to find the similar solutions. Only when the solution cases can not be obtained from the case base or the case solutions are not satisfactory, the RBR system is used to reason for solution and then the reasoning result is stored in the case base for future use. A series experiments are conducted to test its efficiency, which shows that it is superior to the traditional RBR system in response speed. The system is now applied in flood decision support system and city emergency inter-act project.