Meat reduction benefits human and planetary health, but social norms keep meat central in shared meals.To date, the development of _communication strategies that promote meat reduction while minimizing social costs_ has required the costly involvement of human participants at each stage of the process.We present work in progress on simulating multi-round dialogues on meat reduction between _Generative Agents_ based on large language models (LLMs). We measure our main outcome using established psychological questionnaires based on the _Theory of Planned Behavior_ and additionally investigate _Social Costs_.We find evidence that our preliminary simulations produce outcomes that are (i) consistent with theoretical expectations; and (ii) valid when compared to data from previous studies with human participants. Generative agent-based models are a promising tool for identifying novel communication strategies on meat reduction—tailored to highly specific participant groups—to then be tested in subsequent studies with human participants.