While constraints on anglers’ harvest behavior have become increasingly necessary, there is little understanding of angler diversity in preferences for particular management restrictions. This study’s objectives were to understand anglers’ opinions and preferences for management harvest restrictions using a stated preference choice approach (SPCA), to view the diversity of anglers’ opinions and management preferences using the recreation specialization concept, and to suggest feasible management options for fisheries managers. Using a fractional factorial design with seven regulation and expectation attributes required 10 different versions of the mail questionnaire with 8 choice sets each. With an effective response rate of 60%, the final data set included the total responses of 522 red drum anglers with 261, 206, and 55 casual, intermediate, and advanced anglers, respectively. We used conditional logit models to estimate four different preference models including a pooled model for all anglers. As expected, we found that increases in bag limit and maximum size as well as catch probability will lead to considerable increases in the choice of one fishing trip over another. Likewise, anglers preferred a lower minimum size and favored the current two fish over 28” maximum size per year regulation over other options presented. Each specified model of a heterogeneous specialization segment, however, showed different patterns of significant variables. While most variables were statistically significant with the same expected signs, distinctions were noticed. For example, minimum size limit, maximum size limit, average fish size, and expected catch probability were not significant for advanced anglers. Overall, advanced anglers were less interested in relaxing current red drum regulations, while casual anglers showed a strong preference for catching more red drum by relaxing regulations. Results are discussed to help fishery managers take angler diversity into account in future decision-making. Analysis of various scenarios will help optimize the selection of the best combination of regulation attributes.