Economic results of pig-fattening systems vary greatly and depend in part on prices of pork and feeds, as well as pig growth performance (e.g. slaughter weight, lean percentage). Previous studies revealed that feeding and shipping strategies are critical factors in the economic outputs of pig production. However, they failed to consider both strategies and the variability in pig growth performance simultaneously. Consequently, the objective of this study was to develop a new procedure to improve the profitability of pig farms by estimating the best compromise among feeding costs, pork price, animal performance, and shipping constraints. We considered a bi-level programming problem in which the upper-level represents a bioeconomic model that simulates the growth of each pig according to its biological traits whereas the lower-level represents a linear least-cost feed formulation. Bioeconomic decisions taken at the upper-level are live weight at diet changes, the percentage of mean amino acid requirement to be covered at the start of each phase, and the target weight for slaughter. Maximising the mean gross margin per fattened pig is the objective function at the upper-level. It depends on pork price and feeds cost (the objective function of the lower-level) which results from the proportion of each feed ingredient (the lower-level decision variables) at the lower-level. The optimisation problem at the upper-level is solved using an evolutionary algorithm. We considered three sets of prices: average pork and feed prices, high pork and low feed prices, and low pork and high feed prices. The changes in pork prices had major impacts on shipping decisions while had minor impacts on feeding decisions. Optimising the shipping strategy at the same time modified the optimal feeding strategy. Considering the bi-level optimisation model improved the gross margin by 1.65 €/pig (5.2%) compared to the situation where each model (the bioeconomic model and least-cost feed formulation) was optimised separately, and by 3.59 €/pig (11.2%) compared to the common practice on farms in France.