The rapid development of smartphones and smart operating systems boosts the prevalence of natural language conversations in human-machine interactions. In the case of multiple-function tasks, however, the conversation system will generate a complex task command, and a variety of problems will arise. The current NLP technology can provide some solutions, but its capability to dynamically recognize and process task commands is insufficient in solving complex problems. In this study, we propose a solution that combines the NLP engine and task scheduling unit. Specifically, natural language commands are used for task scheduling, and thus the conversation system can accurately recognize command tasks and related parameters and generate a rational schedule for the tasks. In addition, a conversation strategy is proposed to address ambiguity or information omission in the natural language conversation, by which conversation information can be collected with minimum question-answering iterations when necessary.