University course timetabling is a well-known NP-hard combinatorial optimisation problem that involves numerous hard constraints and softer, lecturer-specific preferences. In this study, we propose a two-stage fuzzy-guided genetic algorithm for generating high-quality university schedules, addressing both lecture and laboratory sessions while maintaining their logical dependencies. In the first stage, we construct lecture timetables by enforcing all hard constraints during encoding, while the fitness function evaluates the degree of soft constraint violations, particularly vague lecturer time preferences. These preferences, initially expressed in natural language, are interpreted using a large language model (LLM) and transformed into fuzzy satisfaction scores over time slots. In the second stage, laboratory sessions are scheduled based on the previously generated lecture timetable, ensuring alignment in timing and avoiding conflicts with lecture sessions. We evaluate our method using real data from the Faculty of Computer Science and Engineering at Ho Chi Minh City University of Technology (CSE@HCMUT). The results show that our method consistently produces conflict-free schedules while aligning closely with lecturer preferences, demonstrating the effectiveness of combining LLM-based interpretation and fuzzy evaluation within a genetic optimization framework.