The article is focused on experimental methodology for hypertensive medicament costs comparison by using expert inquiry and evaluation methods. The main goal of this study is to find out comparison between hypertensive therapies by their overall costs. First of all comparison by statistical methods, was performed, but founded final results were not trustful, because the average medicament costs from medicament distributors are subjective. Final cost of therapy comprises even more factors (medicine manufacturer, batch quantity, some medicine is refunded by state and other factors) which are not possible to include in final average cost by using statistical methods. Subjectivity was the main reason why comparison was performed by expert methods, because expert decision about cost can include these additional factors. Based on the fact that the main goal of study is to find therapy cost comparison and not actual average costs, expert inquiry methodology is fully accepted and can be used to solve this task. To find each therapy group cost ratio, pair comparison inquiry procedure is performed. Pair comparison is based on each expert inquiry by giving to fill therapies’ evaluation table. In this evaluation table all therapies are given in pairs and expert has to choose which therapy is more expensive (also equality is accepted). All evaluation tables are summarized and expert consensus level is estimated by using Kendall’s coefficient of concordance, which shows how united is way of thinking of experts. Date statistical trust is verified by finding correlation between entry data and normal distribution. Correlation is founded by Spearman’s rank correlation method which finds distribution between input data. Experimental results show that given information from experts is trustful and it can be comprehended as case related information. At the end therapy cost ratio is calculated and author proposes multiple therapy combination costs comparison algorithm. Further study is related to therapy cost ratio usage for personalized therapy selection for individual patient by using multi objective optimization.