Apriori-based sequential pattern mining algorithms use bottom-up method. They join frequent patterns with shorter length into candidate patterns with longer length, and then repeat the process until no more candidate patterns could be generated. In many applications, only frequent maximal sequential patterns (MSP), which are not a sub-sequence of any other frequent sequential pattern, are requested. In these cases, the Apriori-based algorithms will generate all sequential patterns first, and then eliminate non-maximal ones. That would perform lots of computations not directly related to the final results. For this reason, we propose the Pincer-Style Maximal Sequential Pattern Mining algorithm, PMSPM, to obtain all frequent MSPs by eliminating most of the intermediate steps in the Apriori-based algorithms. Like a pincer’s movement, PMSPM alternates bottom-up and top-down directions to find many MSPs in the early top-down stages. Thus, minings in the bottom-up direction could safely skip many repetitive procedures. PMSPM could save lots of support counting efforts to reduce computing time. We implement PMSPM and compare its performance with that of an Apriori-like algorithm. We also test effects of database parameters on its performance.