Every year UNIMUDA Sorong welcomes new students and keeps promoting to attract more. The process generates a growing number of student data. On the other hand, the promotional strategy to attract new students faces obstacles such as generalization among locations, ineffective time, limited personnel to carry out promotions, and cost inefficiency. This study examines the new student data and university marketing strategies to optimize time, effort, and cost. It uses the K-Means method for data grouping and the Simple Additive Weighting (SAW) for ranking the results of data grouping. The result of this research suggests that the location of promotion may be determined from the clustering process using the K-Means method. The silhouette coefficient test invalidates the data clustering, and the SAW method helps the ranking process to obtain a sequence of promotion locations. The ranking results reflect the predetermined decision table that directs promotion location selection according to the promotion strategy. The combination of the two methods helps to decide the location and marketing strategy to optimize time, effort, and cost. The results of this study may be used as a comparative reference for the management to decide the right promotion strategy based on the locations and student background.