Promotion planning for a store group is a strategic approach aimed at temporarily lowering prices to stimulate sales over a specific period while meeting revenue and margin targets. The challenge lies in selecting the optimal set of products and bundles for promotion and determining their discounts, all within constraints like budget limits, bundle conflicts, and business rules. Conventional methods using linearization or heuristics often lead to sub-optimal solutions, making them unsuitable for real-world scenarios. This paper introduces a two-step Mixed-Integer Nonlinear Programming (MINLP) solution to address the non-linearities inherent in promotion planning. By integrating discrete decisions, such as item selection, with continuous variables like discount levels, MINLP solution offers a comprehensive solution for balancing revenue and margin trade-offs. It allows inclusion of important factors such as hoarding, new bundles and cannibalization effects. In practical settings, where promotions involve about 50–100 SKUs and bundles within a category, the resulting MINLP problem remains computationally feasible and scalable. Compared to the historical discount from a large retailer, promotion using MINLP gives 0.84% increase in revenue and 4.5% increase in margin with 89.9% total budget being used. Considering new bundles improved revenue by 21.32% and margin by 13.96% using 81.22% of total budget. Additionally, detailed what-if analyses provide actionable insights, enabling retailers to evaluate varying strategies, adapt to dynamic market conditions, and strengthen their negotiation positions with vendors. This study highlights the potential of MINLP in transforming promotion planning into a robust and efficient process.