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Emamectin benzoate (EB) is an antiparasitic used to control Caligus rogercresseyi in Chile. However, it has lost efficacy, and the parasite has been exposed to a sublethal dose. Microencapsulation has been suggested as an alternative method to protect and control the release of poorly absorbed drugs to ensure their lethal doses. Accordingly, design of experiments (DOE) and response surface methodology (RSM) were applied to find optimal conditions for EB's ionic gelation (IG) and spray drying (SD) microencapsulation. We used multiobjective (MOO) and multi-response optimization techniques as the desirability function (DFA) to obtain optimal conditions that produce microparticles that satisfy several criteria, such as low gastric digestion (GD) and high yield (Y), encapsulation efficiency (EE), load capacity (LC), and intestinal digestion (ID). The optimization process prioritized the digestion responses and was constrained according to a mass balance. MOO generated theoretical solutions that were better than any of the DOE experimental solutions. Both optimization methods achieved a more balanced performance than the responses obtained in the experimental design. Each optimization method produced better experimental responses than the other in some responses. In SD, DFA yielded higher LC , GD , and ID than MOO by 7.5%, 9.3%, and 2.1%, respectively. In contrast, MOO obtained higher Y and EE than DFA by 6.2% and 10.1%, respectively. In IG, the DFA method yielded a solution with better responses than MOO in LC (3.7%), GD (7.4%), and ID (3.2%), while the MOO solution was better in Y (14.2%) and EE (19.3%). Both multicriteria optimization techniques were suitable for obtaining optimal solutions; however, neither proved superior in all cases. [Display omitted] • Emamectin benzoate was microencapsulated by spray drying and ionic gelation. • Optimization objectives were yield, encapsulation efficiency, load capacity, EB released in gastric and intestinal conditions. • Suitable models for process optimization were fitted according to the response surface methodology. • Multicriteria optimal design was successfully applied to find suitable operating conditions that balanced several objectives. [ABSTRACT FROM AUTHOR]