In ubiquitous sensor networks, extra energy savings can be achieved by selecting the filtering solution to counter the attack. This adaptive selection process employs a fuzzy rule-based system for selecting the best solution, as there is uncertainty in the reasoning processes as well as imprecision in the data. In order to maximize the performance of the fuzzy system the membership functions should be optimized. However, the efforts required to perform this optimization manually can be impractical for commonly used applications. This paper presents a GA-based membership function optimizer for fuzzy adaptive filtering (GAOFF) in ubiquitous sensor networks, in which the efficiency of the membership functions is measured based on simulation results and optimized by GA. The proposed optimization consists of three units; the first performs a simulation using a set of membership functions, the second evaluates the performance of the membership functions based on the simulation results, and the third constructs a population representing the membership functions by GA. The proposed method can optimize the membership functions automatically while utilizing minimal human expertise.