In recent years, received signal strength (RSS) based WiFi fingerprinting positioning technology has gradually become a research hotspot due to its ease of deployment and low cost implementation. However, the positioning accuracy of WiFi fingerprinting positioning based on RSS is affected by many factors. The quality of observed RSS is different among APs due to the complex and time varying indoor environment. Thus the selection of subset of optimal APs has a great influence on the RSS based WiFi fingerprinting positioning. This paper introduces three main AP selection algorithms: joint information gain (JIG) maximization based, mutual information (MI) minimization based and MaxMean (MM) based AP selection strategy, respectively. And the advantages and disadvantages of three different AP selection algorithms are compared and analyzed in this paper. At the same time, the influence of the number of subset of optimal APs and the number of RSS observations at the target point is comprehensively analyzed. Through the experiments, we found that: (1) for all the three algorithms, the positioning results tend to be stable when the number of real time RSS observations at the target point is more than 50; (2) with the given number of real time RSS observation at the target point, the position estimation accuracy change slowly with the increase of number of AP subset when the number of AP subset is more than 5; (3) given that the number of real-time RSS observations at the target point is 50 and the number of subset of optimal APs is 5, the position estimation accuracy of the AP selection strategy based on MI minimization is similar to the AP selection strategy based on MM, and both of them are better than the AP selection strategy based on the JIG maximization.