Enhanced index tracking problem (EITP) aims to add sustainable value to portfolio management by emulating the behavior of the benchmark index while limiting the number of assets it holds. There have been numerous approaches to the EIT problem. Despite this, producing a high-quality portfolio continues to be a challenge. We examine bi-objective enhanced index tracking, which takes both the anticipated excess return of a portfolio associated to the benchmark and the degree of deviation from the benchmark, known as tracking error. In this approach, tracking error and excess return are used to measure the optimization of the problem. The objective of this study is to evaluate the relative efficacy of prominent meta-heuristic evolutionary algorithms NSGA-II, SPEA2, MOEA/D, and MOPSO in addressing enhanced index tracking problems with real-world constraints like a budget constraint, a bound constraint, and a cardinality constraint. Due to these constraints, this problem becomes nondeterministic polynomial-time hard. The paper examines the computational results of real-world benchmark instances. Our proposed methodology has been applied to five data sets derived from significant global markets. Preliminary empirical results show that NSGA-II outperforms other multiobjective evolutionary algorithms.