At present, maritime accidents occur frequently, which brings great harm and loss to the society. It is urgent to use big data to search and rescue emergencies at sea. The drift of the target in distress is a dynamic process of change. The main way to accurately lock its position is to establish communication with it. However, due to the limitations of the reality and technical level and other factors, China maritime search and rescue (S&R) operations are still in the conventional stage, mainly based on artificial blanket search, which cannot achieve large-scale, all-weather search. Therefore, it is difficult to quickly and accurately determine the search target only by relying on the existing maritime S&R decision-making system, So as to delay the best time for S&R. The core problem of optimal search theory is to optimize the allocation of search resources under the condition of limited total search resources and uncertain target distribution, so as to maximize the probability of successful search or minimize the search cost. Therefore, using the optimal search theory to optimize the allocation of search resources can greatly improve the search quality. Then, combined with the research results of the optimal search theory, and taking the target distribution function of the search sub-region as the detection function, the limited search resources (effective search time) are allocated in each search sub-region using the optimal search method. Using this assistant decision-making system to analyze a case of a certain sea area, the result obtained is 16.62% higher than the average performance of the traditional sequential search mode to search for the target, and 15.72% higher than the average performance of the target search by allocating the search time according to the proportion of the target distribution probability. Finally, under the integrated development environment of Microsoft Visual Studio.NET 2003 for Windows system, the development of maritime S&R assistant decision-making system is completed in combination with the electronic chart display and information system (ECDIS) and the marine environment database.