This study proposes a multi-objective optimization approach for industrial park energy management, balancing economic efficiency and grid-friendliness. A comprehensive model of the industrial park is developed, incorporating the linearization of key equipment to formulate the scheduling problem as a Mixed-Integer Linear Programming (MILP) model. The problem is solved using MATLAB, YALMIP, and CPLEX, with an adaptive optimization method introduced to dynamically minimize costs while reducing grid interaction. Simulation results indicate that prioritizing economic efficiency achieves a minimum operational cost of 165,170 CNY, but results in a high maximum grid exchange power of 80 MW. In contrast, optimizing for grid-friendliness eliminates power exchange with the grid but raises operational costs to 259,630 CNY. A Pareto analysis highlights the inherent trade-off between economic feasibility and grid dependence, revealing that an optimal balance can be achieved within a specific range. The proposed method offers a flexible and efficient strategy for industrial park energy scheduling, providing insights into optimizing decision-making under multi-objective constraints while ensuring sustainable and cost-effective operations.