Distribution-free (or nonparametric) monitoring schemes are needed in industrial, chemical and biochemical processes or any other analytical non-industrial process when the assumption of normality fails to hold. The Mann-Whitney (MW) test is one of the most powerful tests used in the design of these types of monitoring schemes. This test is equivalent to the Wilcoxon rank-sum (WRS) test. In this paper, we propose a new distribution-free generally weighted moving average (GWMA) monitoring scheme based on the WRS statistic. The performance of the proposed scheme is investigated using the average run-length, the standard deviation of the run-length, percentile of the run-length and some characteristics of the quality loss function through extensive simulation. The proposed scheme is compared with the existing parametric and nonparametric GWMA monitoring schemes and other well-known control schemes. The effect of the estimated design parameters as well as the effect of the Phase I sample size on the Phase II performance of the new monitoring scheme are also investigated. The results show that the proposed scheme presents better and attractive mean shifts detection properties, and therefore outperforms the existing monitoring schemes in many situations. Moreover, it requires a reasonable number of Phase I observations to guarantee stability and accuracy in the Phase II performance.