This paper introduces DEEM (Differential Evolution with Elitism and Multi-populations), a novel heuristic optimisation algorithm of the Differential Evolution family. DEEM integrates elitism and multi-population strategies to improve convergence speed and accuracy. Additionally, a diversity-based restart strategy is employed to significantly reduce the algorithm's susceptibility to being trapped in local minima. The influence of algorithm parameter choices on optimisation success is demonstrated through a sensitivity study. The algorithm's effectiveness is validated against benchmark functions from CEC 2015, 2017, 2020, and 2022, showing superior performance compared to state-of-the-art DE algorithms. Additionally, DEEM's application is showcased through a complex optimisation problem in the field of geotechnical engineering: the calibration of advanced constitutive models for predicting the stress-strain behaviour of soils under monotonic and cyclic loading. This calibration process is notably time-consuming. DEEM not only achieves better objective values but also does so in fewer iterations, thus significantly reducing computational time.