The development of an evolutionary algorithm and accompanying software for the generation and optimization of artificial-immune-system-based failure detectors using the negative-selection strategy is presented in this paper. A detector is defined as a subregion of the hyperspace formed by relevant system parameters at abnormal conditions. The utility is a part of an integrated set of methodologies for the detection, identification, and evaluation of a wide variety of aircraft subsystem abnormal conditions. The utility is a part of an integrated set of methodologies for the detection, identification, and evaluation of a wide variety of aircraft subsystem abnormal conditions. The process of generating and optimizing detectors has several phases. A preliminary phase consists of processing data from flight tests for self definition, including normalization, duplicate removal, and clustering. A first phase of the evolutionary algorithm produces, through an iterative process, a set of detectors that do not overlap with the self and achieve a prescribed level of coverage of the nonself. A second phase consists of a classic genetic algorithm that attempts to optimize the number of detectors, overlapping between detectors, and coverage of the nonself while maintaining no overlap with the self. For this second phase, an initial individual is a set of detectors obtained in the first phase. Specific genetic operators have been defined to accommodate different detector shapes, such as hyperrectangles, hyperspheres, and hyperellipsoids. An interactive design environment has been developed in MATLAB that relies on an advanced user-friendly graphical interface and on a substantial library of alternative algorithms to allow maximum flexibility and effectiveness in the design of detector sets for artificial-immune-system-based abnormal-condition detection. The desirable performance of the proposed methodology is demonstrated by comparing the detection results for aircraft actuator failures of two unoptimized detector sets with the detection results of an optimized detector set. These results show that the algorithm can determine equal or better detection performance while using fewer detectors to cover the nonself.