The increasing traffic density and complexity of modern airspace require decision-support tools capable of assisting air traffic controllers in resolving conflicts efficiently and safely. This work presents a modular multi-objective framework for trajectory deconfliction in a two-dimensional Air Traffic Control (ATC) environment. Aircraft trajectories are represented through decision points rather than uniform temporal sampling, enabling compact and operationally meaningful descriptions of manoeuvres. The NSGA-II algorithm is used to generate conflict-free alternatives for the aircraft required to deviate from its nominal route. Although the operational environment is planar, each candidate trajectory is reconstructed with associated time and speed profiles, yielding a four-dimensional representation (x, y, t, v) for evaluating separation, path efficiency, and manoeuvrability. The framework simultaneously optimizes path length, and minimizing the deviation from the planned trajectory, producing diverse Pareto-optimal solutions. Experimental results demonstrate consistent generation of feasible and interpretable trajectories, highlighting the suitability of evolutionary multi-objective optimization for ATC conflict resolution and laying the groundwork for future extensions, foremost among them the scaling to three-dimensional trajectories, enabling higher-resolution modeling under uncertainty and enhanced explainability.