The development of the Externalizing Spectrum Inventory (ESI) was motivated by the need to comprehensively assess the interrelated nature of externalizing psychopathology and personality using an empirically driven framework. The ESI measures 23 theoretically distinct yet related unidimensional facets of externalizing, which are structured under 3 superordinate factors representing general externalizing, callous aggression, and substance abuse. One limitation of the ESI is its length at 415 items. To facilitate the use of the ESI in busy clinical and research settings, the current study sought to examine the efficiency and accuracy of a computerized adaptive version of the ESI. Data were collected over 3 waves and totaled 1,787 participants recruited from undergraduate psychology courses as well as male and female state prisons. A series of 6 algorithms with different termination rules were simulated to determine the efficiency and accuracy of each test under 3 different assumed distributions. Scores generated using an optimal adaptive algorithm evidenced high correlations (r > .9) with scores generated using the full ESI, brief ESI item-based factor scales, and the 23 facet scales. The adaptive algorithms for each facet administered a combined average of 115 items, a 72% decrease in comparison to the full ESI. Similarly, scores on the item-based factor scales of the ESI-brief form (57 items) were generated using on average of 17 items, a 70% decrease. The current study successfully demonstrates that an adaptive algorithm can generate similar scores for the ESI and the 3 item-based factor scales using a fraction of the total item pool.