The efficient workforce scheduling amidst constraints related to resources, skills, and language proficiency continues to represent a significant challenge for multilingual contact centers functioning within dynamic operational environments. This research extends the hybrid optimization framework established by Hartmann and Briskorn (2010), which synergistically integrates Genetic Algorithms (GA) and Constraint Programming (CP) for the resolution of Resource-Constrained Project Scheduling Problems (RCPSP). Contemporary scholarly work has augmented this model by incorporating Natural Language Processing (NLP) techniques for the classification of tasks, thereby facilitating a more precise alignment of language and skill sets. Capitalizing on these advancements, the current study employs an NLP-enhanced multi-Skill RCPSP framework specifically designed for internal support teams that face constraints in availability and possess multilingual requirements. A case study was executed that simulated a ticket scheduling scenario involving three distinct support tickets (namely English, Spanish, and French) and two agents exhibiting varying degrees of language proficiency and time constraints. The scheduling dilemma was articulated as a binary integer programming problem with the objective of minimizing Total Weighted Completion Time while concurrently adhering to constraints related to capacity, precedence, and language compatibility. The resultant optimized schedule accomplished a total weighted time of 11, thereby ensuring that high-priority and language-sensitive tickets were addressed in a timely and appropriate manner. The results underscore the efficacy of hybrid optimization models in navigating complex multilingual scheduling challenges, emphasizing the significance of real-time adaptability, workload equilibrium, and intelligent task distribution