Tese (Doutorado)Long Term Evolution has become the de facto technology for the 4G networks. It aims to deliver unprecedented data transmission rates and low latency for several types of applications and services. In this context, this thesis investigates the resource allocation in the LTE uplink. From the principle that resource allocation in the uplink is a complex optimization problem, the main contribution of this thesis is a novel scheduling algorithm based on Genetic Algorithms (GA). This algorithm introduces new operations of initialization, crossover, mutation and a QoS-aware fitness function. The algorithm is evaluated in a mixed traffic environment and its performance is compared with relevant algorithms from the literature. Simulations were carried out in ns-3 and the results show that the proposed algorithm is able to meet the Quality of Service (QoS) requirements of the applications, while presenting a satisfactory execution time.