Transient stability constrained optimal power flow (TSCOPF) is becoming an effective tool for many problems in power systems since it simultaneously considers economy and dynamic stability of system operations. It is increasingly important because modern power systems tend to operate closer to the stability boundaries due to the rapid increase of electricity demand and the deregulation in power sector. TSCOPF is, however, a nonlinear optimization problem with both algebraic and differential equations which is difficult to solve even for small power network. In order to solve the TSCOPF problem efficiently, a relatively new optimization technique, named as krill herd algorithm (KHA), is employed in this paper. KHA simulates the herding behavior of krill swarms in response to specific biological and environmental processes to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. To accelerate the convergence speed and to improve the simulation results, opposition based learning (OBL) is also incorporated in the basic KHA method. The simulation results, obtained by the basic KHA method and the proposed oppositional KHA (OKHA) algorithm, are compared to those obtained by using some other recently developed methods available in the literature. In this paper, case studies conducted on 10 generator New England 39-bus system and 17 generator 162-bus system indicate that the proposed OKHA approach is much more, computationally, efficient than the other reported popular state-of-the-art algorithms including the basic KHA and the proposed method is found to be a promising tool to solve the TSCOPF problem of power systems