The Bin Packing Problem is a combinatorial optimisation problem in which a set of items must be packed into bins while minimising some objective function, usually the number of bins used. This paper examines a variant of practical interest that, instead of focusing solely on spatial resources, aims to optimise temporal resources as well. Existing approaches typically address both objectives through a weighted linear fitness function, combining them via a weighted sum. In this work, we investigate the use of Genetic Programming (GP) to automatically design heuristics in the form of greedy algorithms. As a novel contribution, we integrate GP with NSGA-II to produce a set of non-dominated heuristics that capture different trade-offs between spatial and temporal resources. The results demonstrate that the proposed approach can cover the solution space more comprehensively than GP applied to each criterion independently.